A Novel Approach for Efficient Implementation of Nucleus Detection and Segmentation Using Correlated Dual Color Space

This paper presents a novel algorithm that achieves high performance in leukocytes detection and segmentation in microscopic blood sample images. To the best of our knowledge, this algorithm is the first that exploits the correlation between two CMYK and RGB color models. Our proposed algorithm uses the upper and lower limits of the pixel intensity in the Magenta component from the CMYK color space to adjust the image contrast in the Green component of the RGB image to segment the nuclei without any pre-processing steps. The proposed algorithm was tested using a series of 30 blood sample images with different color tones, image resolutions, magnifications, and surrounding conditions. The segmented nuclei are virtually congruent to the nuclei in the original image, in term of shape and area, regardless of the type of nucleus. The overall segmentation accuracy achieved by the proposed algorithm is 98.99%, with a consistent performance for the same image resolution regardless of the number of nucleus in the blood smear image. Our algorithm can be seen as a stepping stone for efficient implementations of nucleus classification to identify and classify the type of leukocyte for accurate medical diagnosis.