Magnetic Resonance Imaging (MRI) has commonly been used to detect and diagnose brain disease and monitor treatment as non-invasive imaging technology. MRI produces three-dimensional images that help neurologists to identify anomalies from brain images precisely. However, this is a time-consuming and labor-intensive process. The improvement in machine learning and efficient computation provides a computer-aid solution to analyze MRI images and identify the abnormality quickly and accurately. Image segmentation has become a hot and research-oriented area in the medical image analysis community. The computer-aid system for brain abnormalities identification provides the possibility for quickly classifying the disease for early treatment. This article presents a review of the research papers (from 1998 to 2020) on brain tumors segmentation from MRI images. We examined the core segmentation algorithms of each research paper in detail. This article provides readers with a complete overview of the topic and new dimensions of how numerous machine learning and image segmentation approaches are applied to identify brain tumors. By comparing the state-of-the-art and new cutting-edge methods, the deep learning methods are more effective for the segmentation of the tumor from MRI images of the brain.

This paper presents a comprehensive review of the principle and application of deep learning in retinal image analysis. Many eye diseases often lead to blindness in the absence of proper clinical diagnosis and medical treatment. For example, diabetic retinopathy (DR) is one such disease in which the retinal blood vessels of human eyes are damaged. Ophthalmologists diagnose DR based on their professional knowledge, that is labor intensive. With the advances in image processing and artificial intelligence, computer vision based techniques have been applied rapidly and widely in the field of medical images analysis and are becoming a better way to advance ophthalmology in practice. Such approaches utilize accurate visual analysis to identify the abnormality of blood vessels with improved performance over manual procedures. More recently, machine learning, in particular deep learning, has been successfully implemented in this area. In this paper, we focus on recent advances in deep learning methods for retinal image analysis. We review the related publications since 1982, which include more than 80 papers for retinal vessels detections in the research scope spanning from segmentation to classification. Although deep learning has been successfully implemented in other areas, we found only 17 papers so far focus on retinal blood vessel segmentation. This paper characterizes each deep learning based segmentation method as described in the literature. Analyzing along with the limitations and advantages of each method. In the end, we offer some recommendations for future improvement for retinal image analysis.

Accurate segmentation of vessels is an arduous task in the analysis of medical images, particularly the extraction of vessels from colored retinal fundus images. Many image processing tactics have been implemented for accurate detection of vessels, but many vessels have been dropped. In this paper, we propose a deep learning method based on the convolutional neural network (CNN) with dice loss function for retinal vessel segmentation. To our knowledge, we are the first to form the CNN on the basis of the dice loss function for the extraction of vessels from a colored retinal image. The pre-processing steps are used to eliminate uneven illumination to make the training process more efficient. We implement the CNN model based on a variational auto-encoder (VAE), which is a modified version of U-Net. Our main contribution to the implementation of CNN is to replace all pooling layers with progressive convolution and deeper layers. It takes the retinal image as input and generates the image of segmented output vessels with the same resolution as the input image. The proposed segmentation method showed better performance than the existing methods on the most used databases, namely: DRIVE and STARE. In addition, it gives a sensitivity of 0.739 on the DRIVE database with an accuracy of 0.948 and a sensitivity of 0.748 on the STARE database with an accuracy of 0.947.

Content-based image retrieval from large resources has become an area of wide interest in many applications. In this paper we present a CBIR system that uses Ranklet Transform and the color feature as a visual feature to represent the images. Ranklet Transform is proposed as a preprocessing step to make the image invariant to rotation and any image enhancement operations. To speed up the retrieval time, images are clustered according to their features using k-means clustering algorithm.