Exploring CIE Lab Color Characteristics for Skin Lesion Images Detection: A Novel Image Analysis Methodology Incorporating Color-based Segmentation and Luminosity Analysis

 Marwa Mawfaq M. Al-Hatab ^1,*, Ahmed S. Ibrahim Al-Obaidi ², Mohammad Abid Al-Hashim ³

^1,2Technical Engineering College /Northren Technical University, Mosul, Iraq

³Department of Computer Science /Collage of Computer Science and Mathematics / University of Mosul, Iraq

Emails: marwa.alhatab@ntu.edu.iq; ahmedsaeed@ntu.edu.iq;   maqassim@uomosul.edu.iq

Abstract

Accurate classification of malignant and benign skin lesions is crucial in dermatology. In this novel research, we propose robust image analysis methodology for skin lesion classification that integrates color-based segmentation with luminosity analysis. Our approach is evaluated on a dataset of 400 skin images, with equal representation of malignant and benign samples. By computing mean color values for the Red Channel Color (RCC), Green Channel Color (GCC), and Blue Channel Color (BCC) in groups of 10 samples, we establish a classification range for precise diagnosis, this research introduces a novel dimension by harnessing the potential of the CIE Lab Color characteristics for skin lesion detection as the most reliable scale for distinguishing between benign and malignant samples. The smaller and more thought variety ranges saw in the glow examination improve difference and perceivability, consequently working with prevalent sore separation. By featuring the meaning of mean histograms for each variety channel, this complete exploration adds to propelling the area of dermatology and presents an imaginative methodology that holds guarantee for PC helped conclusion frameworks in skin malignant growth discovery.

Keywords: CIE lab color; Image segmentation; skin cancer detection.