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Scene classification is a means to interpret high-resolution remote sensing (HRS) imagery, to obtain the high-level semantic information, which can provide a reliable reference for urban planning and monitoring. The traditional scene classification methods based on HRS imagery take uniform grid cells as the scene units, thereby missing the geographic boundaries and leading to the mosaicking effect. Therefore, in this paper, the urban scene is defined as a geographic unit bordered by the road network. The task of urban scene understanding is to recognize the socioeconomic or natural semantics of the urban scene. However, due to the complexity of the urban environment, the effectiveness of the traditional scene classification methods is limited on account of three problems when applied in urban scenes: 1) The lack of socioeconomic information in HRS images, with which it is difficult to discriminate diverse urban scenes with similar exteriors. 2) The large discrepancy in the sizes and shapes of urban land parcels affects the scene feature extraction and representation. 3) Urban scene understanding frameworks that can embed various scene classification models have rarely been studied. In this paper, to solve these problems, a universal urban scene understanding framework based on multi-source geographic data (USUMG) is proposed. In the USUMG framework, road network and water channel data from OpenStreetMap (OSM) are used for generating the urban scene units. For each irregular unit, a scene decomposition method based on a morphological skeleton is employed to represent the urban scene unit by unified processing patches. To integrate the different data sources, the high-level semantic features extracted from the HRS imagery and the socioeconomic features extracted from point of interest (POI) data are fused to determine the urban scene category. Finally, the USUMG framework with various scene classification methods was tested in urban districts of Wuhan and Macao in China to verify the universality and feasibility of the proposed framework. The experimental performances are provided in this paper as a benchmark for urban scene understanding based on multi-source geographic data.

We present the CPMA, a new method for medial axis pruning with noise robustness and equivariance to isometric transformations. The CPMA leverages the discrete cosine transform to create smooth versions of a shape $\Omega $ . We use the smooth shapes to compute a score function $\mathcal {F}_{\Omega }$ that filters out spurious branches from the medial axis of the original shape $\Omega $ . Our method generalizes to $n$ -dimensional shapes given the properties of the Discrete Cosine Transform. We extensively compare with state-of-the-art pruning methods to highlight the CPMA’s noise robustness and isometric equivariance. We conducted experiments using two 2D datasets — Kimia216 and Animal2000 — and one 3D dataset — the Groningen benchmark. We found that our pruning approach achieves competitive results and yields stable medial axes even in scenarios with significant contour perturbations.

When analyzing financial markets, it needs to mine effective information from massive data. However, it is difficult to obtain information from image information. In order to improve the efficiency of financial market analysis, this paper applies the iris recognition algorithm to financial image data analysis and proposes a feature extraction and recognition algorithm based on morphological skeleton and Gabor filter. The algorithm uses a multi-frequency, multi-directional 2D Gabor filter to extract local features and combines the extracted feature codes with the iris recognition method to complete the identification of intra-class irises and inter-class irises. In addition, in order to verify the effect of the algorithm, this study uses MatalabGUI as a platform to build an experimental model. In summary, in this study, financial images are used as research images for identification and analysis. The research results show that the algorithm proposed in this paper has a certain effect.

International audience; As a main part of several document analysis systems, Skew estimation represents one of the major research challenges, particularly in case of historical documents exploration. In this paper, we propose an original skew angle detection and correction technique. Morphological Skeleton is introduced to considerably diminish the amount of data by eliminating the redundant pixels and preserving only the central curves of the image components. Next, the proposed method uses Progressive Probabilistic Hough Transform (PPHT) to find image lines. At the end, a specific procedure is applied in order to measure the global skew angle of the document image from these identified lines. Experimental results demonstrate the accuracy and the effectiveness of our approach on skew angle detection upon three popular datasets covering many types of documents of diverse linguistic writings (Chinese, Greek and English) and different styles (horizontal or vertical orientations, including figures and tables, multi-columns page layouts).

Suite à la situation actuelle relative au Covid-19, la conférence initialement planifiée pour juillet 2020 a été reporté à avril 2021. La conférence a été réalisé virtuellement par visioconrénce. - Due to the current events related to Covid-19, the conference originally planned for July 2020 has been to April 7-9, 2021. The conference will be organized virtually by videoconference. - Session 11: Image and Video Processing (IVP); International audience

Forward-Looking Sonar (FLS) is one of the most effective devices for underwater exploration which provides high-resolution images that can be used for several tasks in marine research, oceanographic, and deep-sea exploration. The limitation of current underwater acoustic channels does not allow transmitting these images in real-time, therefore image compression is required. Since acoustic images are characterized by speckle noise, an important challenge, in this area, is how to perform the compression while preserving relevant information. In this paper, a novel lossy forward-looking acoustic image compression method based on the combination between keypoint clustering and Morphological Skeleton (MS) is proposed. Keypoints are extracted by using A-KAZE feature extractor, while Density-Based Spatial Clustering of Application with Noise (DBSCAN) is used to find keypoint clusters representing a region-of-interest (ROI). Then, MS is executed to compact the ROI. The rest of the image is down-sampled and quantized through K-Means Clustering and represented via colour indexing. Finally, the information is compressed by using Brotli data compression. The experimental results on real FLS images demonstrate that our method achieves good outcomes in terms of quality metrics and compression ratio.

Background: The purpose of this article is to provide a new evaluation tool based on skeleton maps to assess the tumoral and non-tumoral regions of the 2D MRI in PD-weighted (proton density) and T2w (T2-weighted type) brain images. Methods: The proposed method investigated inter-hemisphere brain tissue similarity using a mask in the right hemisphere and its mirror reflection in the left one. At the hemisphere level and for each ROI (region of interest), a morphological skeleton algorithm was used to efficiently investigate the similarity between hemispheres. Two datasets with 88 T2w and PD images belonging to healthy patients and patients diagnosed with glioma were investigated: D1 contains the original raw images affected by Rician noise and D2 consists of the same images pre-processed for noise removal. Results: The investigation was based on structural similarity assessment by using the Structural Similarity Index (SSIM) and a modified Jaccard metrics. A novel S-Jaccard (Skeleton Jaccard) metric was proposed. Cluster accuracy was estimated based on the Silhouette method (SV). The Silhouette coefficient (SC) indicates the quality of the clustering process for the SSIM and S-Jaccard. To assess the overall classification accuracy an ROC curve implementation was carried out. Conclusions: Consistent results were obtained for healthy patients and for PD images of glioma. We demonstrated that the S-Jaccard metric based on skeletal similarity is an efficient tool for an inter-hemisphere brain similarity evaluation. The accuracy of the proposed skeletonization method was smaller for the original images affected by Rician noise (AUC = 0.883 (T2w) and 0.904 (PD)) but increased for denoised images (AUC = 0.951 (T2w) and 0.969 (PD)).

Deformable medial modeling is an approach to extracting clinically useful features of the morphological skeleton of anatomical structures in medical images. Similar to any deformable modeling technique, it requires a pre-defined model, or synthetic skeleton, of a class of shapes before modeling new instances of that class. The creation of synthetic skeletons often requires manual interaction, and the deformation of the synthetic skeleton to new target geometries is prone to registration errors if not well initialized. This work presents a fully automated method for creating synthetic skeletons (i.e., 3D boundary meshes with medial links) for flat, oblong shapes that are homeomorphic to a sphere. The method rotationally cross-sections the 3D shape, approximates a 2D medial model in each cross-section, and then defines edges between nodes of neighboring slices to create a regularly sampled 3D boundary mesh. In this study, we demonstrate the method on 62 segmentations of placentas in first-trimester 3D ultrasound images and evaluate its compatibility and representational accuracy with an existing deformable modeling method. The method may lead to extraction of new clinically meaningful features of placenta geometry, as well as facilitate other applications of deformable medial modeling in medical image analysis.

International audience; The paper concerns a method for assessing similarity of realisations of random sets based on a construction of their morphological skeletons and a consequent covering of the realisations by unions of the so-called maximal discs. Since the realisations are considered to be binary images, the skeletons together with the corresponding discs can be viewed as realisations of marked point processes with specific properties. A special function for such marked point processes is defined. This function is analogous to the mark-weighted K-function. The function is then used for comparison of given realisations. More precisely, a random sample of the functions is taken from the realisations and the equality in distribution of the functions is tested by an envelope test and by a kernel test. The described procedure is illustrated on a simulation study with the aim to distinguish between realisations coming from different processes and to determine similarity of realisations coming from the same processes.

Microplastics pollution has been recognized as a serious environmental concern, with research efforts underway to determine primary causes. Experiments typically generate bright-field images of microplastic fibers that are filtered from water. Environmental decision making in process engineering critically relies on accurate quantification of mi-croplastic fibers in these images. To satisfy the required standards, images are often analyzed manually, resulting in a highly tedious process, with thousands of fiber instances per image. While the shape of individual fibers is relatively simple, it is difficult to separate them in highly crowded scenes with significant overlap. We propose a fiber instance detection pipeline, which decomposes the fiber detection and segmentation into manageable sub-problems. Well separated instances are identified with robust image processing techniques, such as adaptive thresholding, and morphological skeleton analysis, while tangled fibers are separated by an algorithm based on deep pixel embeddings. Moreover, we present a modified Intersection-over-Union metric as a more appropriate similarity metric for elongated shapes. Our approach improves significantly on out-of-sample data, in particular for difficult cases of intersecting fibers.

Character segmentation is an important task in optical character recognition (OCR). The quality of any OCR system is highly dependent on character segmentation algorithm. Despite the availability of various character segmentation methods proposed to date, existing methods cannot satisfyingly segment characters belonging to some complex writing styles such as the Lanna Dhamma characters. In this paper, a new character segmentation method named graph partitioning-based character segmentation is proposed to address the problem. The proposed method can deal with multi-level writing style as well as touching and broken characters. It is considered as a generalization of existing approaches to multi-level writing style. The proposed method consists of three phases. In the first phase, a newly devised over-segmentation technique based on morphological skeleton is used to obtain redundant fragments of a word image. The fragments are then used to form a segmentation hypotheses graph. In the last phase, the hypotheses graph is partitioned into subgraphs each corresponding to a segmented character using the partitioning algorithm developed specifically for character segmentation purpose. Experimental results based on handwritten Lanna Dhamma characters datasets showed that the proposed method achieved high correct segmentation rate and outperformed existing methods for the Lanna Dhamma alphabet.

Rheumatic Heart Disease is the serious consequence of repeated episodes of Acute Rheumatic Fever, an autoimmune reaction of a group A streptococcal infection. It’s the major cause of heart valve damage, responsible for thousands of deaths per year. The key evidences are changes in the thickness, shape and mobility of the mitral valve leaflets. In this work, an automatic approach is proposed for pathology classification based on motion patterns anterior mitral leaflet, as observed in an echocardiographic video, is proposed. The motion points are extracted from the morphological skeleton of the anterior mitral leaflet and motion velocity and directionality is observed. This data is processed with the Discrete Fourier Transform, and used by several types of classifiers to perform the pathology classification. A Random Forest classifier utilizing the processed data archived a 71% precision and negative predicated value of 58%.

The skeleton of an object provides an intuitive and effective abstraction which facilitates object matching and recognition. However, without any human interaction, traditional skeleton-based descriptors and matching algorithms are not stable for deformable objects. Specifically, some fine-grained topological and geometrical features would be discarded if the skeleton was incomplete or only represented significant visual parts of an object. Moreover, the performance of skeleton-based matching highly depends on the quality and completeness of skeletons. In this paper, we propose a novel object representation and matching algorithm based on hierarchical skeletons which capture the shape topology and geometry through multiple levels of skeletons. For object representation, we reuse the pruned skeleton branches to represent the coarse- and fine-grained shape topological and geometrical features. Moreover, this can improve the stability of skeleton pruning without human interaction. We also propose an object matching method which considers both global shape properties and fine-grained deformations by defining singleton and pairwise potentials for similarity computation between hierarchical skeletons. Our experiments attest our hierarchical skeleton-based method a significantly better performance than most existing shape-based object matching methods on six datasets, achieving a 99.21% bulls-eye score on the MPEG7 shape dataset. HighlightsIt represents the coarse- and fine-grained shape topological and geometrical features.It improves the stability of skeleton pruning without human interaction.It considers both global and fine shape properties by different potential functions.It achieves a better performance than most existing methods on six datasets.Experiments attest our method a better performance than most related approaches.We achieve a 99.21% bulls-eye score on the MPEG7 shape dataset.

The concept of morphological moments of binary images is introduced. Morphological moments can be used as a shape descriptor combining an integral width description of an object with a description of its spatial distribution. The relationship between the proposed descriptor and the disc cover of the figure is discussed and an exact analytical method for descriptor calculation is proposed within the continuous morphology framework. The approach is based on the approximation of the shape by a polygonal figure and the extraction of its medial representation in the form of the continuous skeleton and the radial function. The proposed method for calculation of morphological moments achieves high accuracy and it is computationally efficient. Experimentations have been conducted. Obtained results indicate that the morphological moments are a more informative and rich shape descriptor than the area of the disc cover. Application of morphological moments to the font recognition task improves the recognition quality.

This paper reports the results of a theoretical study on morphological characterization of foreground (X) and background (Xc) of a discrete binary image. Erosion asymmetry and dilation asymmetry, defined to elaborate smoothing of an image respectively by contraction and expansion, are generalized for multiscale smoothing, and their relationships with morphological skeleton and ridge (background skeleton) transformations are discussed. Then we develop algorithms identifying image topology in terms of critical scales corresponding to close-hulls and open-skulls, along with a few other salient characteristics, as respective smoothing by expansion and contraction proceeds. For empirical demonstration of these algorithms, essentially to unravel the hidden characteristics of topological and geometrical relevance, we considered deterministic and random binary Koch quadric fractals. A shape-size based zonal quantization technique for image and its background is introduced as analytical outcome of these algorithms. The ideas presented and demonstrated on binary fractals could be easily extended to the grayscale images and fractals.

This paper presents an efficient framework for solving the problem of static gesture recognition based on data obtained from the web cameras and depth sensor Kinect (RGB-D - data). Each gesture given by a pair of images: color image and depth map. The database store gestures by it features description, genereated by frame for each gesture of the alphabet. Recognition algorithm takes as input a video sequence (a sequence of frames) for marking, put in correspondence with each frame sequence gesture from the database, or decide that there is no suitable gesture in the database. First, classification of the frame of the video sequence is done separately without interframe information. Then, a sequence of successful marked frames in equal gesture is grouped into a single static gesture. We propose a method combined segmentation of frame by depth map and RGB-image. The primary segmentation is based on the depth map. It gives information about the position and allows to get hands rough border. Then, based on the color image border is specified and performed analysis of the shape of the hand. Method of continuous skeleton is used to generate features. We propose a method of skeleton terminal branches, which gives the opportunity to determine the position of the fingers and wrist. Classification features for gesture is description of the position of the fingers relative to the wrist. The experiments were carried out with the developed algorithm on the example of the American Sign Language. American Sign Language gesture has several components, including the shape of the hand, its orientation in space and the type of movement. The accuracy of the proposed method is evaluated on the base of collected gestures consisting of 2700 frames.

Shape representation is an important aspect in image processing and computer vision. There are several skeleton transforms that lead to morphological shape representation algorithm. One of the main problems with these algorithms is in selecting the skeleton points that represent the shape component. If the numbers of skeleton subsets are reduced then the reconstruction process will be easy and time consuming. The present paper proposes a skeleton scheme that selects skeleton points based on the largest shape element. By this, overall skeleton subsets will be reduced. The present method is applied on various images and is compared with generalized skeleton transform and octagon-generating decomposition algorithm.

The mastoid of human temporal bone contains numerous air cells connected to each others. In order to gain further knowledge about these air cells, a more compact representation is needed to obtain an estimate of the size distribution of these cells. Already existing skeletonization methods often fail in producing a faithful skeleton mostly due to noise hampering the binary representation of the data. This paper proposes a different approach by extracting geometrical information embedded in the Euclidean distance transform of a volume via a structure tensor analysis based on quadrature filters, from which a secondary structure tensor allows the extraction of surface skeleton along with a curve skeleton from its eigenvalues. Preliminary results obtained on a X-ray micro-CT scans of a human temporal bone show very promising results.

As an increasing number of digital images are generated, a demand for an efficient and effective image retrieval mechanisms grows. In this work, we present a new skeleton-based algorithm for 2D and 3D shape retrieval. The algorithm starts by drawing circles (spheres for 3D) of increasing radius around skeletons. Since each skeleton corresponds to the center of a maximally inscribed circle (sphere), this process results in circles (spheres) that are partially inside the shape. Computing the ratio between pixels that lie within the shape and the total number of pixels allows us to distinguish shapes with similar skeletons. Experimental evaluation of the proposed approach including a comprehensive comparison with the previous techniques demonstrates both effectiveness and robustness of our algorithm for shape retrieval using several 2D and 3D datasets.

International audience; Skew detection is a crucial step for document analysis systems. Indeed, it represents one of the basic challenges, especially in case of historical documents analysis. In this paper, we propose a novel robust skew angle detection and correction technique. Morphological Skeleton is introduced to significantly reduce the amount of data to treat by removing the redundant pixels and keeping only the central curves of the image components. The proposed method then uses Progressive Probabilistic Hough Transform (PPHT) to identify image lines. A special procedure is finally applied in order to estimate the global skew angle of the document image from these detected lines. Experimental results prove the accuracy and the efficiency of our approach on skew angle detection over three popular datasets containing various types of document of different linguistic writings (such as Chinese, English and Greek) and diverse styles (multi-columns, with figures and tables, vertical or horizontal orientations).

The distance labeled skeleton is a well-known shape representation system that can be used profitably in the framework of shape analysis. A large number of skeletonization algorithms are available in the literature, which are all more or less affected by problems that could limit the actual use of the skeleton for applications. Aim of this contribution is to suggest how to improve the visual aspect of the skeleton and to simplify its structure. In particular, we present tools that can be used to 1) straighten zig-zags possibly present along skeleton branches, 2) replace each set of branch points sufficiently close to each other by the centroid of the zone of influence of the set of branch points, 3) prune branches originating from scarcely significant object's protrusions or arising in correspondence with weak convexities of the object's boundary, 4) perform a polygonal approximation of skeleton branches by taking into account both the changes of curvature along the skeleton branches and the variations in local thickness of the object's regions into which the skeleton branches are mapped, and 5) build a simplified version of the skeleton, where each skeleton branch consists of a sequence of hinged straight-line segments along each of which the distance values of the skeleton points either assume a unique value, or assume values that are monotonically increasing or monotonically decreasing.

A mathematical morphology based approach for color image indexing is explored in this paper. Morphological signatures are powerful descriptions of the image content in the framework of mathematical morphology. A morphological signature (either a pattern spectrum or a differential morphological profile) is defined as a series of morphological operations (namely openings and closings) considering a predefined pattern called structuring element. For image indexing it is considered a morphological feature extraction algorithm which includes more complex morphological operators: i.e. color gradient, homotopic skeleton, Hit-or-Miss transform. In the end, illustrative application examples of the presented approach on real acquired images are also provided.

In binary tomography, the goal is to reconstruct binary images from a small set of their projections. This task can be underdetermined, meaning that several binary images can have the same projections, especially when only one or two projections are given. On the other hand, it is known that a binary image can be exactly reconstructed from its morphological skeleton when all skeletal labels are known. However, if only the skeletal points are given, different labellings yield different reconstructed images. In this paper, we consider a mixture of the above problems, reconstructing a binary image from few projections and the morphological skeleton. We show that the problem is NP-complete, yet a result with low projection and pixel error usually can be achieved, even if only a single projection is available. Three different variants of a method based on Simulated Annealing are developed and compared with respect to reconstruction time and error using artificial binary images.

The morphological skeleton transform (MST) is a leading morphological shape representation scheme. In the MST, a given shape is represented as the union of all the maximal disks contained in the shape. The concept of external skeleton points and external maximal disks has been used for shape description and characterization purposes. In this paper, we develop a generalized morphological skeleton transform that combines the concepts of internal and external maximal disks into a unified framework. In this framework, a shape is described in terms of disk components that need to be added as well as disk components that need to be removed. The procedures and formulae describing the extraction of the disk components and the reconstruction of the original shape from these components are developed. The correctness of the procedures and formulae is established. This new framework seems to provide a more powerful and more natural way of modeling the approximation and reconstruction of binary shapes using primitive shape components.

The curve-skeleton of an object is an important abstract geometrical and topological representation of its shape, which is extremely useful for pattern recognition and computer vision applications involving in shape analysis. In this paper, we propose an effective algorithm for extracting curve skeleton based on the definition and properties of curve skeleton from pixel cloud, which integrates the advantages of the visual main parts reliability for object recognition and the skeletons reduced-dimension for object representation. This algorithm can detect each pixel of the image, and find the salience value of each pixel; the salience value is the possibility of the pixel being a skeleton point. Then an appropriate threshold is selected to pruning the skeleton and to get the curve skeleton. In this way, the algorithm can be effective in reducing the number of non-skeleton pixels, and reduce the overall time of extracting skeleton. The experiments show that the skeleton keeps the topology of the target. And the corners of the skeleton are smoother and more natural. In additionally, it can effectively reduce redundant branches of skeleton.

Iterative morphological binary decomposition (IMBD) is based on first calculating the binary morphological skeleton or binary morphological shape decomposition using digital squares or digital crosses (structuring elements) and then reiterating the method using digital lines. The modified and extended binary digital frames interpolation technique is based on changing some of the IMBD subsets of input frame with the same number of the IMBD subsets of output frame. Simulations results are provided.

This paper presents a skeleton-based method for deforming 2D characters. While previous skeleton-based methods drive the shape deformation by binding the skeleton to the shape, our method does so by propagating the skeleton transformations over the shape. In this way, the tedious process of weight selection in previous skeleton-based methods is not required. Also, the propagation allows us to consider the geometric characteristics of the shape such that local shape distortion can be effectively avoided. Experimental results demonstrate that our method allows real-time deformation and generates visually pleasing results.

Skeletons can be viewed as a compact shape representation in that each shape can be completely reconstructed from its skeleton. However, the usefulness of a skeletal representation is strongly limited by its instability. Skeletons suffer from contour noise in that small contour deformation may lead to large structural changes in the skeleton. A large number of skeleton computation and skeleton pruning approaches has been proposed to address this issue. Our approach differs fundamentally in the fact that we cast skeleton pruning as a trade-off between skeleton simplicity and shape reconstruction error. An ideal skeleton of a given shape should be the skeleton with a simplest possible structure that provides a best possible reconstruction of a given shape. To quantify this trade-off, we propose that the skeleton simplicity corresponds to model simplicity in the Bayesian framework, and the shape reconstruction accuracy is expressed as goodness of fit to the data. We also provide a simple algorithm to approximate the maximum of the Bayesian posterior probability which defines an order for iteratively removing the end branches to obtain the pruned skeleton. Presented experimental results obtained without any parameter tuning clearly demonstrate that the resulting skeletons are stable to boundary deformations and intra class shape variability.

Magnetic resonance imaging (MRI) measurements are essential for the diagnosis of pelvic organ prolapse given the inaccuracy of clinical examination. However, MRI pelvic floor measurements are currently performed manually and can be inconsistent and time-consuming. In this paper, we present a scheme for semi-automatic measurement modeling on MRI based on image segmentation and intersecting point identification methods. The segmentation algorithm is a multi-stage mechanism based on block grouping, support vector machine classification, morphological operation and prior shape information. Block grouping is achieved by classifying blocks as bone or background based on image texture features. The classified blocks are then used to find the initial segmentation by the first phase morphological opening. Prior shape information is incorporated into the initial segmentation to obtain the final segmentation using registration with the similarity type transformation. After segmentation, points of reference that are used for pelvic floor measurements are identified using morphological skeleton operation. The experiments on the MRI images show that the presented scheme can detect the points of reference on the pelvic floor structure to determine the reference lines needed for the assessment of pelvic organ prolapse. This will lead towards more consistent and faster pelvic organ prolapse diagnosis on dynamic MRI studies, and possible screening procedures for predicting predisposition to pelvic organ prolapse by radiologic evaluation of pelvic floor measurements.

Reconstruction of binary images from their projections is one of the main tasks in many image processing areas, therefore determining the computational complexity of those problems is essential. The reconstruction complexity is highly dependent on the requirements of the image. In this paper, we will show that the reconstruction is NP-complete if the horizontal and vertical projections and the morphological skeleton of the image are given, and it is supposed that the image is 4-connected.

Mitochondria exist in a dynamic cycle of fusion and fission whose balance directly influences the morphology of the 'mitochondrial network', a term that encompasses the branched, reticular structure of fused mitochondria as well as the separate, punctate individual organelles within a eukaryotic cell. Over the past decade, the significance of the mitochondrial network has been increasingly appreciated, motivating the development of various approaches to analyze it. Here, we describe the Mitochondrial Network Analysis (MiNA) toolset, a relatively simple pair of macros making use of existing ImageJ plug-ins, allowing for semi-automated analysis of mitochondrial networks in cultured mammalian cells. MiNA is freely available at https://github.com/ScienceToolkit/MiNA. The tool incorporates optional preprocessing steps to enhance the quality of images before converting the images to binary and producing a morphological skeleton for calculating nine parameters to quantitatively capture the morphology of the mitochondrial network. The efficacy of the macro toolset is demonstrated using a sample set of images from SH-SY5Y, C2C12, and mouse embryo fibroblast (MEF) cell cultures treated under different conditions and exhibiting hyperfused, fused, and fragmented mitochondrial network morphologies.