ICTACT Journal on Image and Video Processing

A. Sindhuja ¹, V. Sadasivam ²

Affiliations
1 Department of Computer Science and Engineering, Manonmaniam Sundaranar University, IN
2 PSN College of Engineering and Technology, IN

Abstract

Elastography is the emerging imaging modality that analyzes the stiffness of the tissue for detecting and classifying breast tumors. Computer-aided detection speeds up the diagnostic process of breast cancer improving the survival rate. A multi resolution approach using Discrete wavelet transform is employed on real time images, using the low-low (LL), low-high (LH), high-low (HL), and high-high (HH) sub-bands of Daubechies family. Features are extracted, selected and then finally segmented by K-means clustering algorithm. The proposed work can be extended to Classification of the tumors.

Keywords

Daubechies Wavelet, Feature Selection, SFFS, K-Means.

Full Text

     

D. Jemi Florinabel ¹, S. Ebenezer Juliet ¹, V. Sadasivam ¹

Affiliations
1 Department of Computer Science and Engineering, Manonmaniam Sundaranar University, Tirunelveli, Tamil Nadu, IN

Abstract

A speedy recovery of damaged digitized photographs based on orientation driven multi structure morphology is proposed. The recovery order plays an important factor for human visualization and hence it is guided by the orientation of edges at the surrounding known regions of the missing domain. The image is edge detected by thresholding the image gradient along the eight possible orientations. These eight edge images are represented as eight edge planes. The edge-plane-sliced information is used twice manifold for reconstructing the regions within the missing part, as well as for guiding the integration that follows. The damaged regions are morphologically eroded using the structuring elements of corresponding orientations dictated by the edge-planes. The resultant filled image is obtained using local isotopic driven integration. The novelty of our approach is to explicitly specify the direction of filling herby ensuring ease in convergence in different orientations and then streamlining the process to guarantee complete and natural look. By implementing region-filling through morphological erosion, several pixels instead of one can be restored at every inpainting step, making the method faster than many traditional texture synthesis inpainting algorithms and successfully recovers images with better Peak Signal to Noise ratios even for massive damages.

Keywords

Image Gradient, Morphological Erosion, Structuring Elements, Texture Synthesis, Inpainting.

Full Text

     

B. N. Prathibha ¹, V. Sadasivam ¹

Affiliations
1 Department of Computer Science and Engineering, Manonmaniam Sundaranar University, Tamil Nadu, IN

Abstract

Mammography is a well established imaging technique for showing tissue abnormalities of breast and has been proven to reduce death rate due to breast cancer in screened populations of women. The proposed method classifies the breast tissues according to severity of abnormality (benign or malign) using combined transforms domain features. In this paper two such domains are explored, Discrete Cosine Transform - Discrete Wavelet Transform (DCT-DWT) and Discrete Cosine Transform - Stationary Wavelet Transform (DCT-SWT). The method is tested on 221 mammogram images from the MIAS database. The combined transform domain features proves to be a promising tool for precise classification with SVM classifier. The DCT-DWT domain yields 96.26% accuracy for discrimination between normal-malign samples comparing to DCT-SWT which gives 93.14%. The novelty of the proposed method is demonstrated by comparing with nearest neighbor classification technique.

Keywords

Combined Transforms, Mammograms, SVM, Nearest Neighbor Classifier.

Full Text

     

S. Ebenezer Juliet ¹, V. Sadasivam ¹, D. Jemi Florinabel ¹

Affiliations
1 Department of Computer Science and Engineering, Manomaniam Sundaranar University, Tamil Nadu, IN

Abstract

This paper presents a one pass block classification algorithm for efficient coding of compound images which consists of multimedia elements like text, graphics and natural images. The objective is to minimize the loss of visual quality of text during compression by separating text information which needs high special resolution than the pictures and background. It segments computer screen images into text/graphics and picture/background classes based on DCT energy in each 4x4 block, and then compresses both text/graphics pixels and picture/background blocks by H.264/AVC with variable quantization parameter. Experimental results show that the single H.264/AVC-INTRA coder with variable quantization outperforms single coders such as JPEG, JPEG-2000 for compound images. Also the proposed method improves the PSNR value significantly than standard JPEG, JPEG-2000 and while keeping competitive compression ratios.

Keywords

Compound Image Compression, Block Classification, DCT Coefficients, H.264/AVC-Intra

Full Text

     

B. N. Prathibha ¹, V. Sadasivam ¹

Affiliations
1 Department of Computer Science and Engineering, Manomaniam Sundaranar University, Tamil Nadu, IN

Abstract

Breast cancer is a primary cause of mortality and morbidity in women. Reports reveal that earlier the detection of abnormalities, better the improvement in survival. Digital mammograms are one of the most effective means for detecting possible breast anomalies at early stages. Digital mammograms supported with Computer Aided Diagnostic (CAD) systems help the radiologists in taking reliable decisions. The proposed CAD system extracts wavelet features and spectral features for the better classification of mammograms. The Support Vector Machines classifier is used to analyze 206 mammogram images from Mias database pertaining to the severity of abnormality, i.e., benign and malign. The proposed system gives 93.14% accuracy for discrimination between normal-malign and 87.25% accuracy for normal-benign samples and 89.22% accuracy for benign-malign samples. The study reveals that features extracted in hybrid transform domain with SVM classifier proves to be a promising tool for analysis of mammograms.

Keywords

Mammograms, Classification, Hybrid Transforms, SVM.

Full Text

     

P. Arockia Jansi Rani ¹, V. Sadasivam ²

Affiliations
1 Department of Computer Science and Engineering, Manonmaniam Sundaranar University, Tamil Nadu, IN
2 Department of Computer Science and Engineering, Manonmaniam Sundaranar University, Tamil Nadu,, IN

Abstract

Image compression is very important in reducing the costs of data storage and transmission in relatively slow channels. In this paper, a still image compression scheme driven by Self-Organizing Map with polynomial regression modeling and entropy coding, employed within the wavelet framework is presented. The image compressibility and interpretability are improved by incorporating noise reduction into the compression scheme. The implementation begins with the classical wavelet decomposition, quantization followed by Huffman encoder. The codebook for the quantization process is designed using an unsupervised learning algorithm and further modified using polynomial regression to control the amount of noise reduction. Simulation results show that the proposed method reduces bit rate significantly and provides better perceptual quality than earlier methods.

Full Text

     

G. Muthu Lakshmi ¹, V. Sadasivam ¹

Affiliations
1 Department of Computer Science and Engineering, Manonmaniam Sundaranar University, IN

Abstract

In this paper, a new method of vector quantizer design for image compression using Generic codebook and wavelet transformation is proposed. In the proposed method, Self Organizing Feature Map (SOFM) is used for initial codebook generation. A new scheme of wavelet transformation based Vector Quantization (VQ) technique is proposed to replace the SOFM code vectors by VQ code vectors. The proposed wavelet transform is used to generate wavelet coefficients which are then converted into VQ code vectors. Discrete Cosine Transformation based vector quantization technique is proposed in the existing image compression algorithms with low quality images with greater amount of information loss. Hence to increase the psycho visual quality of the reconstructed image wavelet transformation based vector quantization technique is proposed in this paper. Performance of the proposed work is tested with varying codebook size and various training images. Experimental results show that the reconstructed images obtained by the proposed method are of good quality with better compression ratio and higher Peak Signal–to–Noise Ratio.

Keywords

Vector Quantization, Self-Organizing Feature Map, Image Compression, Wavelet Transformations.

Full Text