Analysis of 2D singularities for mammographic mass classification

Masses are one of the prevalent early signs of breast cancer, visible in mammogram. However, its variation in shape, size, and appearance often creates hazards in proper diagnosis of mammographic masses. This study analyses the 2D singularities of masses and their surrounding regions with Ripplet‐II transform to classify them as benign and malignant. Since benign and malignant masses may change the orientation patterns of normal breast tissues differently, several textural features including Ripplet‐II coefficients and statistical co‐variates, derived from the Ripplet‐II transformed images, are extracted to quantify the texture information of mammographic regions. The important features are then selected using stepwise logistic regression technique and evaluated using linear discriminant analysis and support vector machine with a ten‐fold cross‐validation. The best performance in terms of the area under the receiver operating characteristic curve of 0.91 ± 0.01 and 0.83 ± 0.01 and accuracy of 87.28 ± 0.02 and 75.60 ± 0.01 are obtained with the proposed method while experimenting with 58 images from the mini‐MIAS and 200 images from the Digital Database for Screening Mammography database, respectively

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