Medical Image segmentation is an important image processing step. Comparing images to evaluate the quality of segmentation is an essential part of measuring progress in this research area. Some of the challenges in evaluating medical segmentation are: metric selection, the use in the literature of multiple definitions for certain metrics, inefficiency of the metric calculation implementations leading to difficulties with large volumes, and lack of support for fuzzy segmentation by existing metrics. First we present an overview of 20 evaluation metrics selected based on a comprehensive literature review. For fuzzy segmentation, which shows the level of membership of each voxel to multiple classes, fuzzy definitions of all metrics are provided. We present a discussion about metric properties to provide a guide for selecting evaluation metrics. Finally, we propose an efficient evaluation tool implementing the 20 selected metrics. The tool is optimized to perform efficiently in terms of speed and required memory, also if the image size is extremely large as in the case of whole body MRI or CT volume segmentation. An implementation of this tool is available as an open source project. We propose an efficient evaluation tool for 3D medical image segmentation using 20 evaluation metrics and provide guidelines for selecting a subset of these metrics that is suitable for the data and the segmentation task.

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Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool

The segmentation, detection, and extraction of infected tumor area from magnetic resonance (MR) images are a primary concern but a tedious and time taking task performed by radiologists or clinical experts, and their accuracy depends on their experience only. So, the use of computer aided technology becomes very necessary to overcome these limitations. In this study, to improve the performance and reduce the complexity involves in the medical image segmentation process, we have investigated Berkeley wavelet transformation (BWT) based brain tumor segmentation. Furthermore, to improve the accuracy and quality rate of the support vector machine (SVM) based classifier, relevant features are extracted from each segmented tissue. The experimental results of proposed technique have been evaluated and validated for performance and quality analysis on magnetic resonance brain images, based on accuracy, sensitivity, specificity, and dice similarity index coefficient. The experimental results achieved 96.51% accuracy, 94.2% specificity, and 97.72% sensitivity, demonstrating the effectiveness of the proposed technique for identifying normal and abnormal tissues from brain MR images. The experimental results also obtained an average of 0.82 dice similarity index coefficient, which indicates better overlap between the automated (machines) extracted tumor region with manually extracted tumor region by radiologists. The simulation results prove the significance in terms of quality parameters and accuracy in comparison to state-of-the-art techniques.

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Image Analysis for MRI Based Brain Tumor Detection and Feature Extraction Using Biologically Inspired BWT and SVM

MRI based medical image analysis: Survey on brain tumor grade classification

A distinctive approach in brain tumor detection and classification using MRI

A review on brain tumor segmentation of MRI images.

https://cyberleninka.org/article/n/1240161.pdf

Brain tumor segmentation based on a hybrid clustering technique

Multi-label classification (MLC) is considered an essential research subject in the computer vision field, principally in medical image analysis For this merit, we derive benefits from MLC to diagnose multiple grades of diabetic retinopathy (DR) from various colored fundus images, especially from multi-label (ML) datasets Therefore, ophthalmologists can detect early signs of DR as well as various grades to initiate appropriate treatment and avoid DR complications In this paper, we propose a comprehensive ML computer-aided diagnosis (CAD) system based on deep learning technique The proposed system’s main contribution is to detect and analyze various pathological changes accompanying DR development in the retina without injecting the patient with dye or making expensive scans The proposed ML-CAD system visualizes the different pathological changes and diagnoses the DR grades for the ophthalmologists First, we eliminate noise, enhance quality, and standardize the sizes of the retinal images Second, we differentiated between the healthy and DR cases by calculating the gray level run length matrix average in four different directions The system automatically extracts the four changes: exudates, microaneurysms, hemorrhages, and blood vessels by utilizing a deep learning technique (U-Net) Next, we extract six features, which are the gray level co-occurrence matrix, areas of the four segmenting pathology variations, and the bifurcation points count of the blood vessels Finally, the resulting features were afforded to an ML support vector machine (SVM) based on a classifier chain to differentiate the various DR grades We utilized eight benchmark datasets (four of them are considered ML) and six different performance evaluation metrics to evaluate the proposed system’s performance It achieved 951%, 919%, 861%, 868%, 847%, 862% for accuracy, area under the curve, sensitivity, specificity, positive predictive value, and dice similarity coefficient, respectively The experiments show encouraging results as compared with other systems

/pdf/automatic-diabetic-retinopathy-grading-system-based-on-14l76yxhps.pdf

Automatic Diabetic Retinopathy Grading System Based on Detecting Multiple Retinal Lesions

A comprehensive diagnosis system for early signs and different diabetic retinopathy grades using fundus retinal images based on pathological changes detection

Abstract Diabetic retinopathy (DR) is a serious disease that may cause vision loss unawares without any alarm. Therefore, it is essential to scan and audit the DR progress continuously. In this respect, deep learning techniques achieved great success in medical image analysis. Deep convolution neural network (CNN) architectures are widely used in multi-label (ML) classification. It helps in diagnosing normal and various DR grades: mild, moderate, and severe non-proliferative DR (NPDR) and proliferative DR (PDR). DR grades are formulated by appearing multiple DR lesions simultaneously on the color retinal fundus images. Many lesion types have various features that are difficult to segment and distinguished by utilizing conventional and hand-crafted methods. Therefore, the practical solution is to utilize an effective CNN model. In this paper, we present a novel hybrid, deep learning technique, which is called E-DenseNet. We integrated EyeNet and DenseNet models based on transfer learning. We customized the traditional EyeNet by inserting the dense blocks and optimized the resulting hybrid E-DensNet model’s hyperparameters. The proposed system based on the E-DenseNet model can accurately diagnose healthy and different DR grades from various small and large ML color fundus images. We trained and tested our model on four different datasets that were published from 2006 to 2019. The proposed system achieved an average accuracy (ACC), sensitivity (SEN), specificity (SPE), Dice similarity coefficient (DSC), the quadratic Kappa score (QKS), and the calculation time (T) in minutes (m) equal $$91.2\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mn>91.2</mml:mn><mml:mo>%</mml:mo></mml:mrow></mml:math> , $$96\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mn>96</mml:mn><mml:mo>%</mml:mo></mml:mrow></mml:math> , $$69\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mn>69</mml:mn><mml:mo>%</mml:mo></mml:mrow></mml:math> , $$92.45\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mn>92.45</mml:mn><mml:mo>%</mml:mo></mml:mrow></mml:math> , 0.883, and 3.5 m respectively. The experiments show promising results as compared with other systems. Graphical abstract 

/pdf/a-computer-aided-diagnosis-system-for-detecting-various-12bhlgbt.pdf

A computer-aided diagnosis system for detecting various diabetic retinopathy grades based on a hybrid deep learning technique

Diabetic retinopathy (DR) is a dangerous disease that may cause blindness suddenly without any indications. Therefore, it is necessary to continuously screen and audit the disease progress from the early to severe stages. By nature, the color fundus image may facilitate many lesion types that lead to diagnosing different DR grade. In this respect, deep learning achieved great success in medical image analysis, especially in multi-label (ML) classification. In this paper, we present a novel hybrid, deep learning technique for diagnosing different DR grades, which is called the E-DenseNet model. The proposed technique is a hybrid model of the EyeNet and DenseNet using transfer learning. We got benefits from combining the two models as we customized the EyeNet and embedded dense blocks. The proposed computer-aided diagnosis (CAD) system with E-DenseNet can diagnose different DR grades (normal, mild, moderate, severe, and proliferative DR (PDR)) from various ML color fundus images accurately with minimal training time and memory space. The proposed CAD system gives promising results in diagnosing different DR grades from two benchmark datasets. The proposed system achieved an average accuracy (ACC) equals 91.6%, the Dice similarity coefficient (DSC) equals 92.45%, and the Kappa score equals 0.883.

Eman AbdelMaksoud

Papers

Brain tumor segmentation based on a hybrid clustering technique

Automatic Diabetic Retinopathy Grading System Based on Detecting Multiple Retinal Lesions

A comprehensive diagnosis system for early signs and different diabetic retinopathy grades using fundus retinal images based on pathological changes detection

A computer-aided diagnosis system for detecting various diabetic retinopathy grades based on a hybrid deep learning technique

Diabetic Retinopathy Grading Based on a Hybrid Deep Learning Model