Leukemia is the rapid production of abnormal white blood cells that consequently affects the blood and damages the bone marrow. The overproduction of abnormal and immature white blood cells leads to the damage of the immune system due to the reduced production of red blood cells and platelets by the bone marrow of the body. This hematological malignancy is generally diagnosed by manual methods such as complete blood count (CBC), bone marrow aspiration, or microscopic examination of the blood smear. Nevertheless, the manual methods of leukemia diagnosis are economical but are found to be less reliable, time-consuming, and hectic. Technological advancement in the medical field has effectively addressed these issues in the past. The problems in the manual diagnosis of leukemia detection have been overcome by the development of automated methods using the computer-aided diagnostic (CAD) systems for efficient and reliable leukemia diagnosis. Since the last decade, multiple approaches have been proposed for the CAD systems regarding pre-processing, segmentation, feature extraction, feature selection, and for the improvement of the classification accuracy of the CAD system for the leukemia detection. This paper presents a comprehensive review of the CAD systems for the detection of the various types of leukemia. The review presented here entails the details of various CAD systems for the automated diagnosis of various types of leukemia and analyses their methodologies in terms of their efficiency in pre-processing, segmentation, feature extraction and selection, and overall classification accuracy of the CAD system.

A real-time image filtering technique is proposed which could result in faster implementation for fingerprint image enhancement. One major hurdle associated with fingerprint filtering techniques is the expensive nature of their hardware implementations. To circumvent this, a modified anisotropic Gaussian filter is efficiently adopted in hardware by decomposing the filter into two orthogonal Gaussians and an oriented line Gaussian. An architecture is developed for dynamically controlling the orientation of the line Gaussian filter. To further improve the performance of the filter, the input image is homogenized by a local image normalization. In the proposed structure, for a middle-range reconfigurable FPGA, both parallel compute-intensive and real-time demands were achieved. We manage to efficiently speed up the image-processing time and improve the resource utilization of the FPGA. Test results show an improved speed for its hardware architecture while maintaining reasonable enhancement benchmarks.

The assessment of transformations in the retinal vascular structure has a strong potential in indicating a wide range of underlying ocular pathologies. Correctly identifying the retinal vessel map is a crucial step in disease identification, severity progression assessment, and appropriate treatment. Marking the vessels manually by a human expert is a tedious and time-consuming task, thereby reinforcing the need for automated algorithms capable of quick segmentation of retinal features and any possible anomalies. Techniques based on unsupervised learning methods utilize vessel morphology to classify vessel pixels. This study proposes a directional multi-scale line detector technique for the segmentation of retinal vessels with the prime focus on the tiny vessels that are most difficult to segment out. Constructing a directional line-detector, and using it on images having only the features oriented along the detector’s direction, significantly improves the detection accuracy of the algorithm. The finishing step involves a binarization operation, which is again directional in nature, helps in achieving further performance improvements in terms of key performance indicators. The proposed method is observed to obtain a sensitivity of 0.8043, 0.8011, and 0.7974 for the Digital Retinal Images for Vessel Extraction (DRIVE), STructured Analysis of the Retina (STARE), and Child Heart And health Study in England (CHASE_DB1) datasets, respectively. These results, along with other performance enhancements demonstrated by the conducted experimental evaluation, establish the validity and applicability of directional multi-scale line detectors as a competitive framework for retinal image segmentation.

Accurate vessel segmentation is a tough task for various medical images applications especially the segmentation of retinal images vessels. A computerised algorithm is required for analysing the progress of eye diseases. A variety of computerised retinal segmentation methods have been proposed but almost all methods to date show low sensitivity for narrowly low contrast vessels. We propose a new retinal vessel segmentation algorithm to address the issue of low sensitivity. The proposed method introduces a deep learning model along with pre-processing and post-processing. The pre-processing is used to handle the issue of uneven illuminations. We design a fully Convolutional Neural Network (CNN) and train it to get fine vessels observation.The post-processing step is used to remove the background noise pixels to achieve well-segmented vessels. The proposed segmentation method gives good segmented images especially for detecting tiny vessels. We evaluate our method on the commonly used publicly available databases: DRIVE and STARE databases. The higher sensitivity of 75% leads to proper detection of tiny vessels with an accuracy of 94.7%.