Segmentation of Microscope Erythrocyte Images by CNN-Enhanced Algorithms.

This paper presents an algorithm for segmentation and shape analysis of erythrocyte images collected using an optical microscope. The main objective of the proposed approach is to compute statistical object values such as the number of erythrocytes in the image, their size, and width to height ratio. A median filter, a mean filter and a bilateral filter were used for initial noise reduction. Background subtraction using a rolling ball filter removes background irregularities. Combining the distance transform with the Otsu and watershed segmentation methods allows for initial image segmentation. Further processing steps, including morphological transforms and the previously mentioned segmentation methods, were applied to each segmented cell, resulting in an accurate segmentation. Finally, the noise standard deviation, sensitivity, specificity, precision, negative predictive value, accuracy and the number of detected objects are calculated. The presented approach shows that the second stage of the two-stage segmentation algorithm applied to individual cells segmented in the first stage allows increasing the precision from 0.857 to 0.968 for the artificial image example tested in this paper. The next step of the algorithm is to categorize segmented erythrocytes to identify poorly segmented and abnormal ones, thus automating this process, previously often done manually by specialists. The presented segmentation technique is also applicable as a probability map processor in the deep learning pipeline. The presented two-stage processing introduces a promising fusion model presented by the authors for the first time.