Discriminative Localized Sparse Representations for Breast Cancer Screening

Breast cancer is the most common cancer among women both in developed and developing countries. Early detection and diagnosis of breast cancer may reduce its mortality and improve the quality of life. Computer-aided detection (CADx) and computer-aided diagnosis (CAD) techniques have shown promise for reducing the burden of human expert reading and improve the accuracy and reproducibility of results. Sparse analysis techniques have produced relevant results for representing and recognizing imaging patterns. In this work we propose a method for Label Consistent Spatially Localized Ensemble Sparse Analysis (LC-SLESA). In this work we apply dictionary learning to our block based sparse analysis method to classify breast lesions as benign or malignant. The performance of our method in conjunction with LC-KSVD dictionary learning is evaluated using 10-, 20-, and 30-fold cross validation on the MIAS dataset. Our results indicate that the proposed sparse analyses may be a useful component for breast cancer screening applications

Fundamentals Of Image Compression & Comparative Study Of Relative Study Of JPEG & Hybrid(DWT+DCT) Model

ABSTRACT-In this paper we have discussed the fundamentals of image compression and compared the results of JPEG and a Hybrid model. In hybrid model we have used DWT (Discrete Wavelet Transform) and DCT (Discrete cosine Transform) for transform mapping. As we know that DWT has its multi resolution property which is helpful for image compression. As in JPEG 2000 also DWT is used but EBCOT (Embedded Block Code for Optimal Truncation) coding has high computational complexity. For JPEG we have taken results for different quality factor values and for hybrid model also we have taken results for different quality factor values and compared it is observed that for very less variation in PSNR value compression ratio has increased considerably