Abstract
•Differentiating between benign and malignant tissues based on image intensities.•Solve the topological problems, the white and the gray areas in patient's image.•Formulate the theoretical data optimization problem by applying curve evolution.•The proposed algorithm saves the operation time in addition to improving accuracy.
Identification of tumor in medical images is an important task which leads to increased survival chances. In many cases, tumor diagnosis in medical images is very complicated since the pixel intensities between benign and malignant tissues may be very close. The identification problem is defined as the intensification of the mutual data between the image pixel intensities and the desired areas (tumors in this paper), depending on a limitation of the overall size of the desired area borders. This paper introduces a new approach for image segmentation in differentiating between benign and malignant tissues through a nonparametric approach. The proposed approach assumes that the probability densities correlated with pixel intensities in the image for each region are not already known. The image intensity is used instead of using the probability. A nonparametric density estimation is proposed and formulates the theoretical data optimization problem by applying curve evolution methods and deriving the correlated gradient flows. It uses level-set techniques to achieve the resulting evolution. The algorithm is applied to 100 sets of different real data in patient images with benign or/and malignant tissues. The experimental results show that the proposed algorithm proved effective in tumor identification with an acceptable accuracy of 93.1%, especially in-patient images diagnosed with carcinoma at an early stage.
[Display omitted] In this technique, m level-set functions are used to segment up to 2m areas, and the equation of the resulting evolution curve is proving to be an actual generalization of non-parametric area competition as shown in Fig. 4.