Detection of microcalcifications in mammograms using local maxima andadaptive wavelet transform analysis

A method for computer- aided diagnosis of micro calcification clusters in mammograms images presented . Micro calcification clus.eni which are an early sign of bread cancer appear as isolated bright spots in mammograms. Therefore they correspond to local maxima of the image. The local maxima of the image is lint detected and they are ranked according to it higher-order statistical test performed over the sub band domain data

CH Overtones in Acetophenone and Benzaldehyde: Aryl and Methyl Local Modes

Vibrational overtone spectra of acetophenone and benzaldehyde in the visible and near-infrared regions are studied by the dual beam thermal lens and the conventional near-infrared absorption techniques. The observed increase in the mechanical frequency of the aryl CH bond from that of benzene is attributed to the decrease in the aryl CH bond length caused by the electron-withdrawing property of the substituents. Overtone spectra also demonstrate that acetophenone contains two types of methyl CH bonds arising from the anisotropic environments created by oxygen lone pair and carbonyl P electrons. The local-mode parameters of the two types of CH bonds are compared with those of acetone and acetaldehyde. The possible factors influencing the methyl CH bonds in acetophenone are discussed.

CH local mode overtone excitations in benzyl chloride—A conformational study

The CH local mode overtone spectrum of benzyl chloride in the visible and NIR regions studied by laser induced thermal lens and conventional NIR absorption is presented. The analysis shows that the −CH2Cl group is symmetrically oriented with respect to the benzene ring, thus finalizing one of the two possible conformational models predicted by electron diffraction studies. The aryl CH bonds have a slightly larger force constant than that in benzene.

CBIR Using Local and Global Properties of Image Sub-blocks

This paper proposes a content based image retrieval (CBIR) system using the local colour and texture features of selected image sub-blocks and global colour and shape features of the image. The image sub-blocks are roughly identified by segmenting the image into partitions of different configuration, finding the edge density in each partition using edge thresholding, morphological dilation and finding the corner density in each partition. The colour and texture features of the identified regions are computed from the histograms of the quantized HSV colour space and Gray Level Co- occurrence Matrix (GLCM) respectively. A combined colour and texture feature vector is computed for each region. The shape features are computed from the Edge Histogram Descriptor (EHD). Euclidean distance measure is used for computing the distance between the features of the query and target image. Experimental results show that the proposed method provides better retrieving result than retrieval using some of the existing methods