Analysis and automated classification of images of blood cells to diagnose acute lymphoblastic leukemia

Analysis of white blood cells from blood can help to detect Acute Lymphoblastic Leukemia, a potentially fatal blood cancer if left untreated. The morphological analysis of blood cells images is typically performed manually by an expert; however, this method has numerous drawbacks, including slow analysis, low precision, and the results depend on the operator’s skill. We have developed and present here an automated method for the identification and classification of white blood cells using microscopic images of peripheral blood smears. Once the image has been obtained, we propose describing it using brightness, contrast, and micro-contour orientation histograms. Each of these descriptions provides a coding of the image, which in turn provides n parameters. The extracted characteristics are presented to an encoder’s input. The encoder generates a high-dimensional binary output vector, which is presented to the input of the neural classifier. This paper presents the performance of one classifier, the Random Threshold Classifier. The classifier’s output is the recognized class, which is either a healthy cell or an Acute Lymphoblastic Leukemia-affected cell. As shown below, the proposed neural Random Threshold Classifier achieved a recognition rate of 98.3 % when the data has partitioned on 80 % training set and 20 % testing set for. Our system of image recognition is evaluated using the public dataset of peripheral blood samples from Acute Lymphoblastic Leukemia Image Database. It is important to mention that our system could be implemented as a computational tool for detection of other diseases, where blood cells undergo alterations, such as Covid-1

International Summerschool Computer Science 2014: Proceedings of Summerschool 7.7. - 13.7.2014

Proceedings of International Summerschool Computer Science 201

The use of low-cost photogrammetry techniques to create an accurate model of a human skull

There has been increasing interest in low-cost close range photogrammetry techniques for use in a variety of applications. The use of these techniques in medicine, forensic science, architecture, engineering, archaeology and anthropology to record, measure and monitor objects and sites has been growing in recent years. Close range photogrammetry has been particularly investigated and preferred for human body mapping due to being non-contact, non-invasive, accurate, and inexpensive and data is re-measurable. Skulls have been traditionally measured using callipers and tape in anthropological study, which is subject to observer error. Close range photogrammetry can be used to perform more accurate measurements and retain a digital copy of the skull, which can be re-used for a number of purposes. Using low cost software (Photomodeler), and low cost cameras, the aim of this project is to detail the camera calibration techniques and image capture of a skull. The process for 3D modelling using close range photogrammetry includes camera calibration to determine the camera’s internal parameters, photographing the object within a control target frame, and processing the data with photogrammetry software. Achieving a high precision camera calibration and producing a high-accuracy 3D model were more difficult than anticipated. There are a number of factors which can result in a poor quality models. However, the results show that photogrammetry can be utilised in the capture of accurate 3D skull model using low-cost cameras efficiently. The research was successful, the project objectives were satisfied and the accuracy across the project was approximately 0.4mm

Aerospace Medicine and Biology: A continuing bibliography with indexes

This bibliography lists 253 reports, articles, and other documents introduced into the NASA scientific and technical information system in October 1975

An Image Processing Framework for Breast Cancer Detection Using Multi-View Mammographic Images

Breast cancer is the leading cause of cancer death in women. The early phase of breast cancer is asymptomatic, without any signs or symptoms. The earlier breast cancer can be detected, the greater chance of cure. Early detection using screening mammography is a common step for detecting the presence of breast cancer. Many studies of computer-based using breast cancer detection have been done previously. However, the detection process for craniocaudal (CC) view and mediolateral oblique (MLO) view angles were done separately. This study aims to improve the detection performance for breast cancer diagnosis with CC and MLO view analysis. An image processing framework for multi-view screening was used to improve the diagnostic results rather than single-view. Image enhancement, segmentation, and feature extraction are all part of the framework provided in this study. The stages of image quality improvement are very important because the contrast of mammographic images is relatively low, so it often overlaps between cancer tissue and normal tissue. Texture-based segmentation utilizing the first-order local entropy approach was used to segment the images. The value of the radius and the region of probable cancer were calculated using the findings of feature extraction. The results of this study show the accuracy of breast cancer detection using CC and MLO views were 88.0% and 80.5% respectively. The proposed framework was useful in the diagnosis of breast cancer, that the detection results and features help clinicians in making treatment