Lesion detection and classification in dermoscopic images using optimal threshold based on Newton Raphson iterative method

Abstract

Automatic image thresholding is commonly used in the area of computer vision and pattern recognition for object detection. This research work proposes a new method of lesion detection in dermoscopic images using an optimal threshold based on the Newton-Raphson iteration algorithm for diagnosing melanoma. The proposed method incorporates a new strategy of finding the optimal threshold using first-order and second-order edge derivatives. The first- and second-order derivatives values obtained from the images are applied in the Newton-Raphson method which, in turn, converges to a value after a number of iterations. The converged value is considered the optimal threshold and used as a parameter for the separation of lesion regions from healthy skin in dermoscopic images. To test the proposed system performance, the experiment is carried out on two standard data sets, the ISIC Archive and the PH2, of dermoscopic images using different classifiers such as the Naive Bayes (NB), AdaBoost and Bag of Visual words (BOVW). The proposed segmentation technique segments the lesion regions from skin images and Bag of visual words classifier classifies the dermoscopic images into different classes namely common nevi, atypical nevi and melanoma. The proposed thresholding-based segmentation performance is compared against the Otsu method and Otsu using genetic algorithm. The experimental findings reveal that the proposed Newton-Raphson iterative method surpasses various state-of-the-art and recent methods in relation to computational speed, error rate, sensitivity, and accuracy.