Biomedical Image Processing and Classification

Biomedical image processing is an interdisciplinary field involving a variety of disciplines, e.g., electronics, computer science, physics, mathematics, physiology, and medicine. Several imaging techniques have been developed, providing many approaches to the study of the human body. Biomedical image processing is finding an increasing number of important applications in, for example, the study of the internal structure or function of an organ and the diagnosis or treatment of a disease. If associated with classification methods, it can support the development of computer-aided diagnosis (CAD) systems, which could help medical doctors in refining their clinical picture

Current roles of artificial intelligence in ophthalmology

Artificial intelligence (AI) studies are increasingly reporting successful results in the diagnosis and prognosis prediction of ophthalmological diseases as well as systemic disorders. The goal of this review is to detail how AI can be utilized in making diagnostic predictions to enhance the clinical setting. It is crucial to keep improving methods that emphasize clarity in AI models. This makes it possible to evaluate the information obtained from ocular imaging and easily incorporate it into therapeutic decision-making procedures. This will contribute to the wider acceptance and adoption of AI-based ocular imaging in healthcare settings combining advanced machine learning and deep learning techniques with new developments. Multiple studies were reviewed and evaluated, including AI-based algorithms, retinal images, fundus and optic nerve head (ONH) photographs, and extensive expert reviews. In these studies, carried out in various countries and laboratories of the world, it is seen those complex diagnoses, which can be detected systemic diseases from ophthalmological images, can be made much faster and with higher predictability, accuracy, sensitivity, and specificity, in addition to ophthalmological diseases, by comparing large numbers of images and teaching them to the computer. It is now clear that it can be taken advantage of AI to achieve diagnostic certainty. Collaboration between the fields of medicine and engineering foresees promising advances in improving the predictive accuracy and precision of future medical diagnoses achieved by training machines with this information. However, it is important to keep in mind that each new development requires new additions or updates to various social, psychological, ethical, and legal regulations

Biomedical Image Processing and Classification

Biomedical image processing is an interdisciplinary field [...

LABRAD : Vol 46, Issue 4 - October 2021

Role of Barcoding in a Clinical Laboratory to Reduce Pre-Analytical Errors Congenital Dyserythropoietic Anemia: The Morphological Diagnosis Digital Imaging in Hematology: A New Beginning Metabolomics: Identification of Fatty Acid Oxidation (FAO) Disorders Next-Generation Sequencing for HLA Genotyping Urine Metabolomics to identify Organic Academia Next-Generation Sequencing (NGS) of Solid Tumor Importance of using Genomic Tool in Microbial Identification Radiology Practice in 21st Century: Role of Artificial Intelligence Case Quiz Best of the Recent Past Polaroidhttps://ecommons.aku.edu/labrad/1036/thumbnail.jp

Automated Thalassemia cell image segmentation using hybrid Fuzzy C-Means and K-Means

Thalassemia is a form of hereditary disease. Thalassemia is one of the world's most common illnesses. The morphology of red blood cells is most affected by this disorder. This research proposes a new method of automatically segmenting red blood cells from microscopic blood smear images. The research suggests a novel combination of image processing techniques and extensive preprocessing to achieve superior segmentation performance. In this work, the eleven designated color spaces, with six filters and three contrasts enhancing, Fuzzy c-means and K-means segmentation studied using five evaluation parameters. This evaluation is based on the ground truth image. The Photoshop program performs novel ground truth techniques for multi-object sense (RBC cells). The optimization of all image processing stages was obtained through local image datasets (258 images) obtained from seven thalassemia patients in the Erbil – thalassemia center and five samples of normal blood cells in Children Raparin Teaching Hospital. The image was captured with different light intensities (low, medium, high) and with /without a yellow filter in Biophysics Research lab /Education College / Salahaddin University –Erbil. This study found that the best light intensity for image slide capture utilizing a microscope was medium without using a yellow filter with an accuracy of 0.91± 0.14 and a performance of 95.34%