Enhancement of Chest X-ray Images for Diagnosis Purposes

This study presents image quality comparison between original images and three image enhancement techniques namely imadjust, histogram equalization (HE) and contrast limited adaptive histogram equalization (CLAHE). These techniques are applied to a collection of eight chest x-ray images which are considered as dark, noisy and low in contrast and radiation dosage. The lacks of quality can be solved with these enhancement image techniques. These techniques raised the quality of images and improve the diagnostic ability of the pathological features observed in the images. Then the quality image factors including peak signal-to-noise ratio (PSNR), Mean squared error (MSE), (MAXERR) and (L2rat) were used to evaluate the characteristic of the images. The findings showed that the enhancement techniques managed to enhance the images make them more qualified than original images. Keywords: Histogram Equalization, Contrast Limited Adaptive Histogram Equalization and Image Quality

Enhancement of Chest X-ray Images for Diagnosis Purposes

This study presents image quality comparison between original images and three image enhancement techniques namely imadjust, histogram equalization (HE) and contrast limited adaptive histogram equalization (CLAHE). These techniques are applied to a collection of eight chest x-ray images which are considered as dark, noisy and low in contrast and radiation dosage. The lacks of quality can be solved with these enhancement image techniques. These techniques raised the quality of images and improve the diagnostic ability of the pathological features observed in the images. Then the quality image factors including peak signal-to-noise ratio (PSNR), Mean squared error (MSE), (MAXERR) and (L2rat) were used to evaluate the characteristic of the images. The findings showed that the enhancement techniques managed to enhance the images make them more qualified than original images. Keywords: Histogram Equalization, Contrast Limited Adaptive Histogram Equalization and Image Quality

Mammography Image Enhancement using Linear, Nonlinear and Wavelet Filters with Histogram Equalization

In the worldwide, breast cancer is one of the major diseases among the women. In the modern medical science, there are plenty of newly devised methodologies and techniques for the timely detection of breast cancer. However, there are difficulties still exist for detecting breast cancer at an early stage for its diagnoses because of poor visualization and artifacts present in the mammography. Thus the Digital mammographic image preprocessing often requires, enhancement of the image to improve the quality while preserving important details. The proposed method works in three stages. First it removes all the artifacts present in the image. Second it denoise the image by using Linear, nonlinear and wavelet filters. Third, contrast of the image increased by histogram equalization. This method definitely helps to computer aided diagnosis system to increase the accuracy. The experimental results are tested on two standard datasets MIAS and DDSM.

HISTOGRAM NORMALIZATION TECHNIQUE FOR PREPROCESSING OF DIGITAL MAMMOGRAPHIC IMAGES

Digital mammogram has become the most efficient tool for early breast cancer detection modalities and pre-processing these images requires high computational capabilities. Pre-processing is one of the most important step in the mammogram analysis due to poor captured mammographic image qualities. Pre-processing is basically used to correct and adjust the mammogram image for further study and classification.  Many image pre-processing techniques have been developed over the past decades to help radiologists in diagnosing breast cancer. Most studies conducted have proven that a pre-processed image is easier for radiologist to accurately detect breast cancer especially for dense breast. Different types of techniques are available for pre-processing of mammograms, which are used to improve image quality, remove noise, adjust contrast, enhance the image and preserve the edges within the image. This paper acquired 20 digital mammograms from Mammographic Image Analysis Society (MIAS) database and uses Histogram Normalization algorithm for pre-processing of the mammograms. A percentage of 95% was obtained. It was observed that the pre-processed mammographic images displayed breast abnormalities clearer with little or no noise

A new preprocessing filter for digital mammograms

This paper presents a computer-aided approach to enhancing suspicious lesions in digital mammograms. The developed algorithm improves on a well-known preprocessor filter named contrast-limited adaptive histogram equalization (CLAHE) to remove noise and intensity inhomogeneities. The proposed preprocessing filter, called fuzzy contrast-limited adaptive histogram equalization (FCLAHE), performs non-linear enhancement. The filter eliminates noise and intensity inhomogeneities in the background while retaining the natural gray level variations of mammographic images within suspicious lesions. We applied Catarious segmentation method (CSM) to compare the segmentation accuracy in two scenarios: when there is no preprocessing filter, and when the proposed preprocessing filter is applied to the original image. The proposed filter has been evaluated on 50 real mammographic images and the experimental results show an average increase of segmentation accuracy by 14.16% when the new filter is applied