Analysis of Various Textural Descriptors for Ovarian Cyst Classification

Ovarian cyst is one of the main causes of infertility. Ovarian cancers are also caused by the ovarian cyst that grows in the ovary. An ovarian cyst can be benign (non-cancerous) or malignant (cancerous). If the cyst is not diagnosed and treated at the earliest, the benign cyst may turn into malignant and can be fatal. Various image processing techniques are used to assist the clinicians to characterize the ovarian cyst using the textural descriptor. This paper reviews several textural descriptors for feature extraction like Local Binary Pattern (LBP), Local Directional Pattern (LDP) and Local Optimal Oriented Pattern (LOOP). Finally, extracted features are applied to SVM, KNN and Ensemble classifiers to compare the performance of the textural descriptors.

A Secure Boot Loader System for Loading an Operating System with Fingerprint Authentication

In today’s world, people are constantly on move and portable systems are in demand. With technology advancement, people exploit different types of memory devices for a portable system. For any external boot medium, the BIOS boot order setting change is required. The dynamic boot loader successfully eliminated this dependency and allowed the user to directly boot from any portable USB. The usage of USB has grown exponentially in recent years and securing it has become a major concern. In this paper, the USB is devised as a highly secured portable boot medium with fingerprint authentication to ensure data security. It performs feature extraction by combining both Local Directional Pattern (LDP) and Histograms of Oriented Gradients (HOG) which improves the accuracy rate. The classification is performed by random forest classifier, such that the intended users alone are granted access to the private storage area of the USB drive.