This paper presents a preliminary analysis of a class of non-linear filters for enhancement of mammogram lesions. A non-linear filtering approach employing polynomial model of non-linearity is designed by second order truncation of Volterra series expansion. The proposed filter response is a linear combination of Type-0 and Type-II Volterra filters. Type-0 filter provides contrast enhancement, suppressing the ill-effects of background noise. On the other hand, Type-II filter employs edge enhancement. The objective analysis of the proposed filter is carried out by estimating values of quality parameters like CEM and PSNR on mammograms from MIAS and DDSM databases.

A Polynomial filtering model for enhancement of mammogram lesions

Unified Preprocessing and Enhancement Technique for Mammogram Images

A new algorithm for feature and contrast enhancement of mammographic images is proposed in this paper. The approach bases on multiscale transform and mathematical morphology. First of all, the Laplacian Gaussian pyramid operator is applied to transform the mammography into different scale subband images. In addition, the detail or high frequency subimages are equalized by contrast limited adaptive histogram equalization (CLAHE) and low-pass subimages are processed by mathematical morphology. Finally, the enhanced image of feature and contrast is reconstructed from the Laplacian Gaussian pyramid coefficients modified at one or more levels by contrast limited adaptive histogram equalization and mathematical morphology, respectively. The enhanced image is processed by global nonlinear operator. The experimental results show that the presented algorithm is effective for feature and contrast enhancement of mammogram. The performance evaluation of the proposed algorithm is measured by contrast evaluation criterion for image, signal-noise-ratio (SNR), and contrast improvement index (CII).

https://downloads.hindawi.com/journals/cmmm/2013/716948.pdf

Feature and Contrast Enhancement of Mammographic Image Based on Multiscale Analysis and Morphology

Non-linear filters have the property to enhance and preserve edges of lesion. Detection of edges of tumour is required in application such as to evaluate effectiveness of breast cancer treatment. Proposed algorithm uses polynomial filtering technique to enhance the contrast of lesion while preserving its edges with effective suppression of background noise. This algorithm successfully detected edges of lesion in digital mammograms taken from DDSM mammogram database. Also different metrics were used to establish effectiveness of enhancement and noise suppression in mammograms.

Volterra Filter design for edge enhancement of mammogram lesions

A new algorithm for feature and contrast enhancement of mammographic images is proposed in this paper. The approach is based on multiscale transform and mathematical morphology. First of all, the Laplacian Gaussian pyramid transform is applied to decompose the mammography into different multiscale subband sub-images. In addition, the detail or high frequency sub-images are equalized by the contrast limited adaptive histogram equalization (CLAHE) and low frequency sub-images are processed by mathematical morphology. Finally, the enhanced image of feature and contrast is reconstructed from the Laplacian Gaussian pyramid coefficients modified at one or more levels by CLAHE and mathematical morphology. The enhanced image is processed by global non-linear operator in order to obtain natural result. The experimental results show that the presented algorithm is effective for feature and contrast enhancement of mammogram. The performance evaluation of the proposed algorithm is contrast evaluation criterion for image, signal-noise-ratio (SNR) and contrast improvement index (CII).

Feature and contrast enhancement of mammographic image based on multiscale analysis and morphology

Mammography is an effective method for breast cancer detection and breast tumor analysis. In mammography, low dose x-ray is used for imaging, due to which the images are poor in contrast and are contaminated by noise. Hence it is difficult for the radiologist to screen the mammograms for diagnostic signs such as micro calcifications and masses. This ensures the need for image enhancement to aid radiologist. In this paper we present a different algorithm for enhancement of digital mammographic images. The proposed methodology uses mathematical morphology for contrast enhancement and wavelet for denoising. The main contribution of this paper is in differentiating the edge pixels from noise. A quantitative measure of Contrast Improvement Index (CII) and Edge Preservation Index (EPI) are used to evaluate the performance of the algorithm. The algorithm has been tested on a large number of images from standard dataset, comparing the results with the state-of-the- art. By both the analytical indices and ROC analysis, the proposed algorithm shows promising results in early detection of breast cancer and diagnosis.

Mammographic image enhancement using modified mathematical morphology and Bi-orthogonal wavelet

Segmentation of lung regions with lung nodules at mediastinum is the first step in computer-aided detection (CAD) which provides a better diagnosis of lung cancer. The existing methods fail in segmentation of lung regions with the cancer tumors at the mediastinum of the lungs. In this paper, a new approach is proposed that extracts lung regions with cancer tumors at the mediastinum of the lungs based on curve analysis. The proposed algorithm is tested on 05 patient’s dataset which consists of 60 images of the Lung Image Database Consortium (LIDC) and the results are found to be satisfactory with 99 % average overlap measure (AΩ). The proposed algorithm extracts lung nodules at the mediastinum of the lungs which helps in detection of lung cancer.

Automatic Extraction of Lung Regions Using Curve Analysis for the Detection of Lung Cancer in CT Scans

Ultrasound imaging is a widely used diagnostic technique for the early detection of breast diseases. However, the usefulness of ultrasound imaging is degraded by the multiplicative speckle noise. This reduces the efficiency of diagnosis by radiologists. In order to improve the efficiency of diagnosis, we propose an algorithm for speckle denoising and edge enhancement for the segmentation of ROI. The algorithm is performed in three steps. In the first step, speckle denoising is achieved through shrinkage based on local variance matrix. The second step enhances the edges based on formation of homogenous blocks. The third steps segments the object boundaries based on K-means clustering algorithm. The results of the proposed method have been compared with the well known filters. The experimental results show that the proposed algorithm has considerably improved the image quality without providing any noticeable artifact.

M. Ravi Shankar

Papers

Mammographic image enhancement using modified mathematical morphology and Bi-orthogonal wavelet

Automatic Extraction of Lung Regions Using Curve Analysis for the Detection of Lung Cancer in CT Scans

Speckle Noise Reduction in Breast Ultrasound Images for Segmentation of Region Of Interest (ROI) Using Discrete Wavelets