4 research outputs found
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