Accurate segmentation of brain tumor from MRI is crucial in computer aided diagnosis as well as in other medical imaging applications. Brain tumor segmentation is a challenging task due to the diverse appearance of tumor tissues. A variety of brain tumor segmentation techniques have been explored in the literature. Here, a brief review of different brain tumor segmentation techniques has been discussed with their merits and demerits. We conclude with a discussion on the trend of future research in brain tumor segmentation.
Interstitial lung disease (ILD), representing a collection of disorders, is considered to be the deadliest one, which increases the mortality rate of humans. In this thesis, an automated scheme for detection and classification of ILD patterns is presented, which eliminates low inter-class feature variation and high intra-class feature variation in patterns, caused by translation and illumination variation effects. Region of interests (ROIs) of four lung tissue patterns (healthy, emphysema, ground glass and fibrosis) selected from a publicly available database are identified and utilized after pre-processing them. A novel and efficient approach for feature extraction is proposed using template matching combined sparse coding, which extracts features invariant to translation and illumination effects, from defined regions of interest (ROIs) within lung parenchyma. The system is tested by a K-nearest neighbor (Knn) classifier, exploiting the sparse coded features. Performance of the proposed scheme was evaluated using various measures such as recall, specificity accuracy, F-score and precision. The proposed scheme shows a better performance for the value K=100 of Knn classifier with an overall accuracy of 81.3%.
Non-cooperative iris recognition system results in blurred and noisy images that can degrade the performance of the system. This paper proposes a novel feature extraction algorithm for non-cooperative environment. The proposed iris subdivision method reduces the effect of noise due to non-cooperative behaviour in an authentication system. Optimal projection analysis is proposed to obtain the rotation invariant features from the directional subband coefficients of the normalised image block. A fusion algorithm combines the matching scores from individual subimages to reduce the false rejection rate. The performance of the proposed method is compared with other algorithms on UBIRIS iris image database.
In Satellite Images, enhancement plays a dynamic research topic in image processing. The aim of enhancement is to process an image so that the result is more suitable than original image for specific remote sensing application. Satellite image enhancement techniques provide a lot of choices for improving the visual quality of remotely sensed images. In this research review, image fusion plays an important role, since it effectively combines auxiliary image content to enhance information contained in the individual datasets. This article provides an overview of the existing enhancement techniques. There are many techniques which have been proposed for enhancing the digital images which may be used for enhancing Satellite images. Here, a survey on various Satellite image enhancement techniques has been performed which recommends fusion-based enhancement performs superior while comparing with non-fusion-based enhancement techniques.
Background: Segmentation of brain tumor from Magnetic Resonance Imaging (MRI) becomes very complicated due to the structural complexities of human brain and the presence of intensity inhomogeneities. Objective: To propose a method that effectively se
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