Learn More
Wavelet transform (WT) is a potential tool for the detection of microcalcifications, an early sign of breast cancer. This article describes the implementation and evaluates the performance of two novel WT-based schemes for the automatic detection of clustered microcalcifications in digitized mammograms. Employing a one-dimensional WT technique that utilizes(More)
Simple manual RTs to lateral visual stimuli were found to be faster in uncrossed than in crossed anatomical conditions. Neither hand nor hemifield effects were found. By contrast significant asymmetries were found in Choice RTs involving both hemispheres, even in uncrossed condition. The field effect was marked when the responding hand was the right but(More)
Alxtmcf-In this paper, wc prcscr a novel approach to the problem of computer-aided analysis of digital mammograms for breast cancer detection. The algorithm devcloped herc classifics mammograms into norma; & abnormal. First, thc structures in mammograms produced by normal glandular tissue of varying dcnsity are eliminated using a Wavelet Transform (WT)(More)
Every year, several terabytes of image data-both medical and non medical-are engendered so that the requisition for image compression is substantiated. In this paper, the correlation properties of wavelets are harnessed in linear predictive coding to compress images. The image is decomposed using a one dimensional wavelet transform. The highest level(More)
This paper presents a novel approach to the fast localization and extraction of optic disc from fundus images of the human retina. Optic disc continues to be a major landmark for fundus image registration and is indispensible for the understanding of retinal fundus images. For the detection of optic disc, we first decompose the image into its bit planes.(More)
Medical image Classification can play an important role in diagnostic and teaching purposes in medicine. For these purposes different imaging modalities are used. There are many classifications created for medical images using both grey-scale and color medical images. One way is to find the texture of the images and have the analysis. Texture classification(More)
  • 1