Diagnosis Support of Sickle Cell Anemia by Classifying Red Blood Cell Shape in Peripheral Blood Images
Wilkie Delgado-Font, Miriela Escobedo-Nicot, Manuel Gonz\'alez-Hidalgo, Silena Herold-Garcia, Antoni Jaume-i-Cap\'o, Arnau Mir

TL;DR
This paper presents an automated image analysis method for classifying red blood cell shapes in blood smear images to support sickle cell anemia diagnosis, improving accuracy over existing techniques.
Contribution
The study introduces a novel automated approach combining segmentation and shape analysis descriptors, with elliptical adjustments for occluded cells, outperforming state-of-the-art methods.
Findings
Achieved F-measure of 0.97 for normal cells
Achieved F-measure of 0.95 for elongated cells
Demonstrated superior performance in sickle cell diagnosis
Abstract
Red blood cell (RBC) deformation is the consequence of several diseases, including sickle cell anemia, which causes recurring episodes of pain and severe pronounced anemia. Monitoring patients with these diseases involves the observation of peripheral blood samples under a microscope, a time-consuming procedure. Moreover, a specialist is required to perform this technique, and owing to the subjective nature of the observation of isolated RBCs, the error rate is high. In this paper, we propose an automated method for differentially enumerating RBCs that uses peripheral blood smear image analysis. In this method, the objects of interest in the image are segmented using a Chan-Vese active contour model. An analysis is then performed to classify the RBCs, also called erythrocytes, as normal or elongated or having other deformations, using the basic shape analysis descriptors: circular shape…
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Taxonomy
TopicsDigital Imaging for Blood Diseases · Blood properties and coagulation · AI in cancer detection
