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A novel approach for segmentation and counting of overlapped leukocytes in microscopic blood images

Sudha K., Geetha P.

Biocybernetics and Biomedical Engineering

2020

Vol. 40, no. 2

639--648

Leukocytes count in the blood smear images plays an important role in identifying the overall health of the patient. The major steps involved in leukocytes counting system are segmentation and counting. However, the counting accuracy is greatly affected due to the morphological diversity of cells, the presence of staining artifacts and the overlapped cells. Therefore, this paper introduces a new framework to segment and counting of leukocytes. To segment leukocytes, an edge strength-based Grabcut method has been proposed. Later, the leukocyte region including the overlapped cells was counted using the novel gradient circular hough transform (GCHT) method. The research work was performed on ALL-IDB and Cellavision datasets. The proposed segmentation method has yielded high precision, recall and f -measure compared to the state-of-the-art methods. Additionally, comparison analy-sis was performed between the region count obtained using the existing and the GCHT method. The overall experimental results of the work showed that the proposed framework produced more accuracy in counting the leukocytes.

A full reference Morphological Edge Similarity Index to account processing induced edge artefacts in magnetic resonance images

Kuppusamy P. G., Joseph J., Sivaraman J.

Biocybernetics and Biomedical Engineering

2017

Vol. 37, no. 1

159--166

An objective measure of edge similarity between the original and processed images to quantify the processing induced artefacts in medical image computing is proposed in this article. Globally accepted Image Quality Analysis (IQA) indices such as Peak Signal Noise Ratio (PSNR) and Structural Similarity Index Metric (SSIM) measure the structural similarity between the original and processed image and do not specifically reflect the resemblance of the edge content. Most of the IQA indices either do not comply with the subjective quality ratings or they are prone to noise level. In the proposed Morphological Edge Similarity Index (MESI), the binary edge maps of the reference and processed images are generated via gradient based threshold and these edge maps are objectively compared to yield a reliable edge quality metric. The index is found superior to Edge Preservation Index (EPI), Edge Strength Similarity based Image quality Metric (ESSIM) and SSIM in terms of dynamic variability, correlation with subjective quality ratings, robustness to noise and sensitivity to degradation in edge quality caused by blockiness artefacts in image compression. MESI exhibits a correlation of 0.9985, very close to unity, with the subjective quality ratings. It is useful for objectively evaluating the performance of denoising, sharpening and enhancement schemes and for the selection of optimum value of the arbitrary parameters used in them.