Applications of Extendable Embedded Web Servers in Medical Diagnosing

Advances in Healthcare Information Systems and Administration

2018

Diagnosis of cancer is of prime concern in recent years. Medical imaging is used to analyze these diseases. But, these images contain uncertainties due to various factors and thus intelligent techniques are essential to process these uncertainties. This chapter highlights two hybridizations pertaining to breast cancer. In one hybridization technique, it hybridizes intuitionistic fuzzy set and rough set in combination with statistical feature extraction methods. In the second case, intuitionistic fuzzy histogram hyperbolization is hybridized with possibilistic fuzzy c-mean clustering algorithm. Both hybridizations are studied to extract the region of interest and then to enhance the edges surrounding it. Experimental analysis is carried out for both models and an exhaustive study on these models is presented in this chapter.

Breast Cancer Detection Using Hybrid Computational Intelligence Techniques

Acharjya

Advances in Healthcare Information Systems and Administration

2018

Breast Cancer Detection Using Hybrid Computational Intelligence Techniques

Acharjya

Research Anthology on Medical Informatics in Breast and Cervical Cancer

2022

Simple Linear Iterative Clustering (SLIC) and Graph Theory-Based Image Segmentation

Handbook of Research on Machine Learning Techniques for Pattern Recognition and Information Security

2021

With the extensive application of deep acquisition devices, it has become more feasible to access deep data. The accuracy of image segmentation can be improved by depth data as an additional feature. The current research interests in simple linear iterative clustering (SLIC) are because it is a simple and efficient superpixel segmentation method, and it is initially applied for optical images. This mainly comprises three operation steps (i.e., initialization, local k-means clustering, and postprocessing). A scheme to develop the image over-segmentation task is introduced in this chapter. It considers the pixels of an image with simple linear iterative clustering and graph theory-based algorithm. In this regard, the main contribution is to provide a method for extracting superpixels with greater adherence to the edges of the regions. The experimental tests will consider biomedical grayscales. The robustness and effectiveness will be verified by quantitative and qualitative results.