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The signed pressure force (SPF) function has recently become a popular function for guiding the curve evolution of the active contour model (ACM) for image segmentation. The aim is to extract the boundaries of digital medical images for shape and image analysis. The recent SPF-based ACM demonstrates effectiveness in image segmentation. However, it may fail if the targeted object is close to a neighbouring object. Additionally, the presence of intensity inhomogeneity and noise in medical images degrades segmentation accuracy and local target areas. Thus, we proposed a new SPF-based ACM, namely the Selective Segmentation with Signed Pressure Force 1 (SSPF1) model, by incorporating the ideas of the SPF function and the distance fitting term based on geometrical constraints. Then, the new SSPF1 model was extended by incorporating an image enhancement technique to develop our second new model, termed the Selective Segmentation with Signed Pressure Force 2 (SSPF2). Numerical results indicated that the SSPF2 model was more recommended than SSPF1 as the SSPF2 model was approximately 4.7% more accurate, as indicated by the Jaccard value and was about 112 times faster in segmenting noisy images compared to the existing selective segmentation model

The region-based Active Contour Model (ACM) is a widely known variational segmentation model for extracting or segmenting a digital image into numerous sections for further analysis. Distinguishing between global and specific segmentation models within this paradigm is possible. The global segmentation model is incapable of selectively segmenting the region of interest (ROI) from the input image, which leads to an over-segmented problem. A variety of models have been devised to address the task of selective segmentation, which involves the extraction of the boundary of a particular region of interest (ROI) inside a digital image. The Primal Dual Selective Segmentation (PDSS) model has been recently introduced and exhibits significant potential in terms of accuracy. Nevertheless, the presence of intensity inhomogeneity in digital images disrupts the precision and localisation of target regions of segmentation. Therefore, it is important to take into account bias field adjustment, also known as correction for intensity inhomogeneity. So, this study came up with a new selective segmentation model called the Selective Segmentation with Bias Field Correction (SSBF) model by combining the idea of the existing PDSS model with the level set-based bias field correction technique. To solve the proposed SSBF model, we first derived the Euler-Lagrange (EL) equation and solved it in MATLAB software. The Intersection over Union (IOU) coefficient, also known as the Dice (DSC) and Jaccard (JSC) similarity metrics, evaluated the proposed model's accuracy. Experimental results demonstrate that the JSC and DSC values of the proposed model were 13.4 and 7.2 higher, respectively, than the competing model.

Mammography is a screening tool for breast cancer detection that produces grayscale images of the breast. The fundamental problem in mammography image analysis is to extract the boundary of breast abnormality from its healthy background tissues. The process is also known as the image segmentation. The procedure is necessary for further clinical diagnosis and monitoring in Computer Aided Detection (CAD) analysis systems. Active contour method has been proven to be effective to extract boundary of an image. The recent and effective selective type of active contour model, termed Primal Dual Selective Segmentation (PDSS) model, was proposed in 2019. However, the PDSS model having problem in segmenting images with low contrast. It is known that low contrast image is commonly encountered in mammography images that can result to poor boundary extraction. Thus, the aim of this study is to modify the PDSS model to extract the boundary of abnormality region in mammography images. The modification is made by considering three different image enhancement algorithms which are histogram equalization, histogram stretching and adaptive histogram equalization as the new fitting terms in the PDSS model and these results in three variants of modified PDSS models termed as PDSS1, PDSS2 and PDSS3 respectively. The efficiency of the proposed models was then assessed by recording the computation time while the accuracy of the extracted image boundary was evaluated using the Jaccard (JSC) and Dice Similarity Coefficients (DSC). Numerical experiments demonstrated that the proposed PDSS2 model based on histogram stretching achieved the highest segmentation accuracy with the fastest computational speed compared to other models. In future, the proposed model can be extended into the threedimensional and colour formulations.

One of the most important steps in image processing and computer vision for image analysis is segmentation, which can be classified into global and selective segmentations. Global segmentation models can segment whole objects in an image. Unfortunately, these models are unable to segment a specific object that is required for extraction. To overcome this limitation, the selective segmentation model, which is capable of extracting a particular object or region in an image, must be prioritised. Recent selective segmentation models have shown to be effective in segmenting greyscale images. Nevertheless, if the input is vector-valued or identified as a colour image, the models simply ignore the colour information by converting that image into a greyscale format. Colour plays an important role in the interpretation of object boundaries within an image as it helps to provide a more detailed explanation of the scene’s objects. Therefore, in this research, a model for selective segmentation of vector-valued images is proposed by combining concepts from existing models. The finite difference method was used to solve the resulting Euler-Lagrange (EL) partial differential equation of the proposed model. The accuracy of the proposed model’s segmentation output was then assessed using visual observation as well as by using two similarity indices, namely the Jaccard (JSC) and Dice (DSC) similarity coefficients. Experimental results demonstrated that the proposed model is capable of successfully segmenting a specific object in vector-valued images. Future research on this area can be further extended in three-dimensional modelling.

Image segmentation can be defined as partitioning an image that contains multiple segments of meaningful parts for further processing. Global segmentation is concerned with segmenting the whole object of an observed image. Meanwhile, the selective segmentation model is concerned with segmenting a specific object required to be extracted. The Convex Distance Selective Segmentation (CDSS) model, which uses the Euclidean distance function as the fitting term, was proposed in 2015. However, the Euclidean distance function takes time to compute. This paper proposes the reformulation of the CDSS minimization problem by changing the fitting term with three popular distance functions, namely Chessboard, City Block, and Quasi-Euclidean. The proposed models are CDSSNEW1, CDSSNEW2, and CDSSNEW3, which apply the Chessboard, City Block, and Quasi-Euclidean distance functions respectively. In this study, the Euler-Lagrange (EL) equations of the proposed models were derived and solved using the Additive Operator Splitting method. Then, MATLAB coding was developed to implement the proposed models. The accuracy of the segmented image was evaluated using the Jaccard (JSC) and Dice Similarity Coefficients (DSC). The execution time was recorded to measure the efficiency of the models. Numerical results showed that the proposed CDSSNEW1 model based on the Chessboard distance function could segment the specific object successfully for all grayscale images with the fastest execution time compared to other models.

Edge detection has been widely used especially in medical image processing field. In this paper we are comparing Sobel, Prewitt and Laplacian of Gaussian (LoG) edge detection techniques in segmenting the boundary of microcalcifications. The edge detection must satisfy the breast phantom scoring criteria before the segmentation phase is carried out. Then, all of the edge detection techniques are implemented in the Enhanced Distance Active Contour (EDAC) model for the segmentation process. Results obtained from Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC) curve shows that the Prewitt edge detection has the highest value of AUC, followed by the Sobel and LoG which are 0.79, 0.72 and 0.71 respectively.

The active contour or Snake is a computer generated curve, used to trace boundaries of images. This paper presents the application of Snake for the segmentation of masses on breast ultrasound images and the characterization of the segmented masses as malignant or benign. Initially, the Balloon Snake is chosen to segment the masses. Comparison on the masses areas segmented by the Balloon Snake is done against the areas traced by radiologist. Experimental result shows that from fifty masses tested, the Balloon Snake successfully segment the masses with accuracy of 95.71%. Then, a mass is characterized as benign or malignant using a proposed method namely the semi-automated characterization (SAC) method. The method is based on the segmented masses produced by the Balloon Snake. The criterion of angular margin is considered in characterizing the masses as malignant or benign by the SAC method. The characterization reading of a mass by the SAC method is compared with thirty sets of characterization readings of a mass by different radiologists. The comparison is made in terms of sensitivity and specificity values. Based on the values, the receiver operating characteristics (ROC) curve is plotted for each set of comparison. From the thirty sets of comparisons, it is found that the area under curve of all the thirty ROC curves are greater than 0.7. The value implies that the SAC method gives high accuracy in characterizing benign from malignant mass. Since the method is based on the segmented masses by the Balloon Snake, the value also implies that the accuracy of Balloon Snake in segmenting the images is high (95.71%).

Snake, active contour or deformable active contour has been widely used in medical image segmentation area. In this paper, comparison between Gradient Vector Flow (GVF) snake and Enhanced Distance (ED) snake in segmenting microcalcifications is carried out. The performance is measured based on actual area of the average percentage difference traced by expert radiologists. Results obtained shows that the values of average percentage difference for the GVF and ED snake are 4.3% and 6.68% respectively. These results indicate that the GVF snake has better performance with 95.7%.

In this paper an image segmentation technique is presented by combining seed based region growing and boundary segmentation in sequential order. The first process in region growing is to identify an initial seed point. Most of region growing methods identify the seed point manually which involve human interaction. Thus, automated initial seed point identification for region growing algorithm is proposed. The boundary segmentation technique is implemented in order to improve the segmentation results. The method is tested on 50 mammogram images confirmed by a radiologist to consist microcalcifications. Experimental results show that the algorithm successfully segment the microcalcifications with accuracy of 0.94.

This paper presents the application of Active Contour or Snake for the segmentation of masses on breast ultrasound images and the characterization of the segmented masses as malignant or benign. Initially, the Balloon Snake is chosen to segment the masses. Comparison on the masses areas segmented by the Balloon Snake is done against the areas traced by radiologist. Experimental result shows that from fifty masses tested, the Balloon Snake successfully segment the masses with accuracy of 95.71%. Then, a segmented mass by the Balloon Snake is characterized as benign or malignant based on the measurement of angular margin. The mass is considered as a malignant if it has at least one acute angular margin. Otherwise it is a benign. The characterization of a mass by the Balloon Snake based on the criterion of angular margin is compared with the actual type of a mass characterized by an expert radiologist. The comparison is made in terms of sensitivity and specificity values. Based on the values, the receiver operating characteristics (ROC) curve is plotted. It is found that the area under curve is 0.8455. The value implies that the Balloon Snake gives high accuracy in characterizing benign from malignant mass based on the criterion used.

This paper presents the application of Active Contour or Snake for the segmentation of masses on breast ultrasound images and the characterization of the segmented masses as malignant or benign. Initially, the Balloon Snake is chosen to segment the masses. Comparison on the masses areas segmented by the Balloon Snake is done against the areas traced by radiologist. Experimental result shows that from fifty masses tested, the Balloon Snake successfully segment the masses with accuracy of 95.71%. Then, a segmented mass by the Balloon Snake is characterized as benign or malignant based on the measurement of angular margin. The mass is considered as a malignant if it has at least one acute angular margin. Otherwise it is a benign. The characterization of a mass by the Balloon Snake based on the criterion of angular margin is compared with the actual type of a mass characterized by an expert radiologist. The comparison is made in terms of sensitivity and specificity values. Based on the values, the receiver operating characteristics (ROC) curve is plotted. It is found that the area under curve is 0.8455. The value implies that the Balloon Snake gives high accuracy in characterizing benign from malignant mass based on the criterion used.