Differential count of leukocytes plays a consequential role in the determination of diagnostic details of a patient. Conventionally, leukocyte identification is performed manually by skilled medical personnel. But the involvement of humans slows down the task and also affects the process outcome. Designing a computer-aided system to automate this task can reduce the load on the medical personnel and help in attaining precise results in minimal time. This research presents an image-processing technique to segment the Leukocyte Nucleus from the blood smear image and categorize them into their constituent classes based on the features drawn from the segmented region. Mathematical Operations are performed on the color channels of RGB and LAB color spaces to obtain Nucleus-enhanced grayscale Image. Leukocyte Nucleus is segmented using Fuzzy C-Means Clustering. Geometrical, Color and Texture features are extracted from the segmented nucleus. Feed-Forward Neural Network and Support Vector Machine classifiers are employed to classify the leukocytes into its constituent classes. The proposed segmentation technique rendered better segmentation performance compared to the methods reviewed in the literature with an overall segmentation accuracy of 88.1%. The Feed Forward Neural Network classifier yielded an accuracy of 92.8% for Leukocyte classification.
|Journal||International Journal of Computers and Applications|
|Publication status||Published - 17-08-2020|
All Science Journal Classification (ASJC) codes
- Hardware and Architecture
- Computer Science Applications
- Computer Graphics and Computer-Aided Design