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Background Lung region segmentation is an important stage of automated image-based approaches for the diagnosis of respiratory diseases. Manual methods executed by experts are considered the gold standard, but it is time consuming and the accuracy is dependent on radiologists’ experience. Automated methods are relatively fast and reproducible with potential to facilitate physician interpretation of images. However, these benefits are possible only after overcoming several challenges. The traditional methods that are formulated as a three-stage segmentation demonstrate promising results on normal CT data but perform poorly in the presence of pathological features and variations in image quality attributes. The implementation of deep learning methods that can demonstrate superior performance over traditional methods is dependent on the quantity, quality, cost and the time it takes to generate training data. Thus, efficient and clinically relevant automated segmentation method is desired for the diagnosis of respiratory diseases. Methods We implement each of the three stages of traditional methods using deep learning methods trained on five different configurations of training data with ground truths obtained from the 3D Image Reconstruction for Comparison of Algorithm Database (3DIRCAD) and the Interstitial Lung Diseases (ILD) database. The data was augmented with the Lung Image Database Consortium (LIDC-IDRI) image collection and a realistic phantom. A convolutional neural network (CNN) at the preprocessing stage classifies the input into lung and none lung regions. The processing stage was implemented using a CNN-based U-net while the postprocessing stage utilize another U-net and CNN for contour refinement and filtering out false positives, respectively. Results The performance of the proposed method was evaluated on 1230 and 1100 CT slices from the 3DIRCAD and ILD databases. We investigate the performance of the proposed method on five configurations of training data and three configurations of the segmentation system; three-stage segmentation and three-stage segmentation without a CNN classifier and contrast enhancement, respectively. The Dice-score recorded by the proposed method range from 0.76 to 0.95. Conclusion The clinical relevance and segmentation accuracy of deep learning models can improve though deep learning-based three-stage segmentation, image quality evaluation and enhancement as well as augmenting the training data with large volume of cheap and quality training data. We propose a new and novel deep learning-based method of contour refinement.

Presence of clutters and occluding objects within agricultural farm environments challenges accurate segmentation of plant leaves, a prerequisite for an effective machine-vision-based automation of agricultural tasks. In this paper, we propose a plant leaves segmentation method that can be integrated into vision-based robotic harvester and quality inspection systems. The proposed method combines the discriminatory power of color-based technique with the simplicity and computational efficiency of threshold-based technique. Clutters and occluding objects are eliminated by infinitesimal angular displacement of the threshold image, followed by the application of set theory. Performance evaluation shows that the proposed method demonstrate strong robust features and computational efficiency.

Background: Rician noise, bias felds and blur are the common distortions that degrade MRI images during acquisition. Blur is unique in comparison to Rician noise and bias felds because it can be introduced into an image beyond the acquisition stage such as postacquisition processing and the manifestation of pathological condi tions. Most current blur assessment algorithms are designed and validated on con sumer electronics such as television, video and mobile appliances. The few algorithms dedicated to medical images either requires a reference image or incorporate manual approach. For these reasons it is difcult to compare quality measures from diferent images and images with diferent contents. Furthermore, they will not be suitable in environments where large volumes of images are processed. In this report we propose a new blind blur assessment method for diferent types of MRI images and for diferent applications including automated environments. Methods: Two copies of the test image are generated. Edge map is extracted by sepa rately convolving each copy of the test image with two parallel diference of Gaussian flters. At the start of the multiscale representation, the initial output of the flters are equal. In subsequent scales of the multiscale representation, each flter is tuned to diferent operating parameters over the same fxed range of Gaussian scales. The flters are termed low and high energy flters based on their characteristics to successively attenuate and highlight edges over the range of multiscale representation. Quality score is predicted from the distance between the normalized mean of the edge maps at the fnal output of the flters. Results: The proposed method was evaluated on cardiac and brain MRI images. Performance evaluation shows that the quality index has very good correlation with human perception and will be suitable for application in routine clinical practice and clinical research. © The Author(s) 2018. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/)

Magnetic resonance imaging (MRI) system images are important components in the development of drugs because it can reveal the underlying pathology in diseases. Unfortunately, the processes of image acquisition, storage, transmission, processing, and analysis can influence image quality with the risk of compromising the reliability of MRI-based data. Therefore, it is necessary to monitor image quality throughout the different stages of the imaging workflow. This report describes a new approach to evaluate the quality of an MRI slice in multi-center clinical trials. The design philosophy assumes that an MRI slice, such as all natural images, possess statistical properties that can describe different levels of contrast degradation. A unique set of pixel configuration is assigned to each possible level of contrast-distorted MRI slice. Invocation of the central limit theorem results in two separate Gaussian distributions. The central limit theorem says that the mean and standard deviation of pixel configuration assigned to each possible level of contrast degradation will follow a normal distribution. The mean of each normal distribution corresponds to the mean and standard deviation of the underlying ideal image. Quality prediction processes for a test image can be summarized into four steps. The first step extracts local contrast feature image from the test image. The second step computes the mean and standard deviation of the feature image. The third step separately standardizes each normal distribution using the mean and standard deviation computed from the feature image. This gives two separate z-scores. The fourth step predicts the lightness contrast quality score and the texture contrast quality score from cumulative distribution function of the appropriate normal distribution. The proposed method was evaluated objectively on brain and cardiac MRI volume data using four different types and levels of degradation. The four types of degradation are Rician noise, circular blur, motion blur, and intensity nonuniformity also known as bias fields. Objective evaluation was validated using a proposed variation of difference of mean opinion scores. Results from performance evaluation show that the proposed method will be suitable to monitor and standardize image quality throughout the different stages of imaging workflow in large clinical trials. MATLAB implementation of the proposed objective quality evaluation method can be downloaded from (https://github.com/ezimic/Image-Quality-Evaluation)., This report describes a new approach to evaluate the quality of an MRI cardiac or brain slice in multi-center clinical trials.

The authors propose a new application-specific, post-acquisition quality evaluation method for brain magnetic resonance imaging (MRI) images. The domain of a MRI slice is regarded as universal set. Four feature images; greyscale, local entropy, local contrast and local standard deviation are extracted from the slice and transformed into the binary domain. Each feature image is regarded as a set enclosed by the universal set. Four qualities attribute; lightness, contrast, sharpness and texture details are described by four different combinations of feature sets. In an ideal MRI slice, the four feature sets are identically equal. Degree of distortion in real MRI slice is quantified by fidelity between the sets that describe a quality attribute. Noise is the fifth quality attribute and is described by the slice Euler number region property. Total quality score is the weighted sum of the five quality scores. The authors' proposed method addresses current challenges in image quality evaluation. It is simple, easy-to-use and easy-to-understand. Incorporation of binary transformation in the proposed method reduces computational and operational complexity of the algorithm. They provide experimental results that demonstrate efficacy of their proposed method on good quality images and on common distortions in MRI images of the brain. © The Institution of Engineering and Technology 2017. This is the authors’ accepted and refereed manuscript to the article.