A Review on Automated Brain Tumor Detection and Segmentation from MRI of Brain

Tumor segmentation from magnetic resonance imaging (MRI) data is an important but time consuming manual task performed by medical experts. Automating this process is a challenging task because of the high diversity in the appearance of tumor tissues among different patients and in many cases similarity with the normal tissues. MRI is an advanced medical imaging technique providing rich information about the human soft-tissue anatomy. There are different brain tumor detection and segmentation methods to detect and segment a brain tumor from MRI images. These detection and segmentation approaches are reviewed with an importance placed on enlightening the advantages and drawbacks of these methods for brain tumor detection and segmentation. The use of MRI image detection and segmentation in different procedures are also described. Here a brief review of different segmentation for detection of brain tumor from MRI of brain has been discussed.

Feature Extraction of Human Lip Prints

Methods have been used for identification of human by recognizing lip prints. Human lips have a number of elevation and depressions features called lip prints and examination of lip prints is referred to as cheiloscopy. Lip prints of each human being are unique in nature like many others features of human. In this paper lip print is first smoothened using a Gaussian Filter. Next Sobel Edge Detector and Canny Edge Detector are used to detect the vertical and horizontal groove pattern in the lip. This method of identification will be useful both in criminal forensics and personal identification. It is our assumption that study of lip prints and their types are well connected to play a song in a better way that are well accepted to people who loves to hear songs.

Personal Identification from Lip-Print Features using a Statistical Model

This paper presents a novel approach towards identification of human beings from the statistical analysis of their lip prints. Lip features are extracted by studying the spatial orientations of the grooves present in lip prints of individuals using standard edge detection techniques. Horizontal, vertical and diagonal groove features are analysed using connected-component analysis to generate the region-specific edge datasets. Comparison between test and reference sample datasets against a threshold value to define a match yield satisfactory results. FAR, FRR and ROC metrics have been used to gauge the performance of the algorithm for real-world deployment in unimodal and multimodal biometric verification systems.