Using vector quantization for image processing

2013

IJIGSP

-Breast cancer is most common and leading cause of death among women. With improvement in the imaging modalities it is possible to diagnose the cancer at an early stage moreover treatment at an early stage reduces the mortality rate. B-mode ultrasound (US) imaging is very illustrious and reliable technique in early detection of masses in the breast. Though it is complimentary to the mammography, dense breast tissues can be examined more efficiently and detects the small nodules that are usually not observed in mammography. Segmentation of US images gives the clear understanding of nature and growth of the tumor. But some inherent artifact of US images makes this process difficult and computationally inefficient. Many methods are discussed in the literature for US image segmentation, each method has its pros and cons. In this paper, initially region merging based watershed and marker-controlled watershed transforms are discussed and implemented. In the subsequent sections we proposed a method for segmentation, based on clustering. Proposed method consists of three stages, in first stage probability images and its equalized histogram images are obtained from the original US images without any preprocessing. In the next stage, we used VQ based clustering technique with LBG, KPE and KEVR codebook generation algorithm followed by sequential cluster merging. Last stage is the post processing, where we removed unwanted regions from the selected cluster image by labeling the connected components and moreover used morphological operation for closing the holes in the final segmented image. Finally, results by our method are compared with initially discussed methods.

Section: Vector Quantizationmentioning

confidence: 99%

Tumour Delineation using Statistical Properties of The Breast US Images and Vector Quantization based Clustering Algorithms

Kekre¹,

2013

IJIGSP

“…Many codebook generation and quantization algorithms were developed and tested successfully to achieve good compression ratio discussed in R. M. Gray [20], Pamela C. [21], and Huang C. [22]. Currently researcher focusing on the usefulness of this technique in other image processing domains such as content based image retrieval by H. B. Kekre [23], pattern recognition and face identification by Qiu Chen [34], and C. Garcia [24], segmentation of MRI and Mammogram images By H. B. Kekre [25,26], iris and finger print recognition etc.…”

Section: Vector Quantizationmentioning

confidence: 99%

Tumor Demarcation of Breast US Images using VQ Based Clustering Algorithms on Proba bility and its Histogram Equalized Images

Kekre¹,

2014

IJAST

“…Vector Quantization (VQ) was initially developed and implemented for image compression, with the help of many codebook generation and quantization algorithms [25,26,27,28], but now a days it has been extensively use in other applications, such as image segmentation [29], speech recognition [30], pattern recognition and face detection [31,32], tumor demarcation in MRI and Mammogram images [33,34], content based image retrieval [35,36] etc. In this paper, this method has been used as clustering aid in demarcation of area of interest (cysts and tumor) in breast ultrasound images.…”

Section: Vector Quantizationmentioning

confidence: 99%

Segmentation of Ultrasound Breast Images using Vector Neighborhood with Vector Sequencing on KMCG and augmented KMCG algorithms

Kekre¹,

2013

IJACSA

Abstract-B mode ultrasound (US) imaging is popular and important modality to examine the range of clinical problems and also used as complimentary to the mammogram imaging to detect and diagnose the nature breast tumor. To understand the nature (benign or malignant) of the tumor most of the radiologists focus on shape and boundary. Therefore boundary is as important characteristic of the tumor along with the shape. Tracing the contour manually is a time consuming and tedious task. Automated and efficient segmentation method also helps radiologists to understand and observe the volume of a tumor (growth or shrinkage). Inherent artifact present in US images, such as speckle, attenuation and shadows are major hurdles in achieving proper segmentation. Along with these artifacts, inhomogeneous texture present in the region of interest is also a major concern. Most of the algorithms studies in the literature include noise removal technique as a preprocessing step. Here in this paper, we are eliminating this step and directly handling the images with high degree of noise. VQ based clustering technique is proposed for US image segmentation with KMCG and augmented KMCG codebook generation algorithms. Using this algorithm images are divided in to clusters, further these clusters are merged sequentially. A novel technique of sequential cluster merging with vector sequencing has been used. We have also proposed a technique to find out the region of interest from the selected cluster with seed vector acquisition.Results obtained by our method are compared with our earlier method and Marker Controlled Watershed transform. With the opinion of the expert radiologist, we found that our method gives better results.