Contrast Enhancement Of Mammograms Research Articles

Contrast Enhancement of Mammograms and Microcalcification Detection

Mammography is an operative procedure for early detection of cancer present in breast. However, the pathological changes of the breast are difficult to interpret from low contrast mammograms. This research proposes a method to enhance the contrast of the mammogram that uses Non-subsampled contourlet transform (NSCT) based edge information. Instead of a directional filter bank in the conventional NSCT structure, this paper uses multiscale non-separable edge filters. These edge filters outputs intrinsic edge structure information based on simplified hyperbolic tangent function applied with two polarized schemes. This edge information further used to improve the local contrast. Adaptive histogram equalization (AHE) also used to increase the overall contrast of mammogram. Improved detection of microcalcification (MC) from enhanced mammogram images shows the success of this algorithm. This method has better enhancement measure (EME) than AHE and unsharp based mammogram enhancement method.

Local Binary Patterns Descriptor Based on Sparse Curvelet Coefficients for False-Positive Reduction in Mammograms.

Breast Cancer is the most prevalent cancer among women across the globe. Automatic detection of breast cancer using Computer Aided Diagnosis (CAD) system suffers from false positives (FPs). Thus, reduction of FP is one of the challenging tasks to improve the performance of the diagnosis systems. In the present work, new FP reduction technique has been proposed for breast cancer diagnosis. It is based on appropriate integration of preprocessing, Self-organizing map (SOM) clustering, region of interest (ROI) extraction, and FP reduction. In preprocessing, contrast enhancement of mammograms has been achieved using Local Entropy Maximization algorithm. The unsupervised SOM clusters an image into number of segments to identify the cancerous region and extracts tumor regions (i.e., ROIs). However, it also detects some FPs which affects the efficiency of the algorithm. Therefore, to reduce the FPs, the output of the SOM is given to the FP reduction step which is aimed to classify the extracted ROIs into normal and abnormal class. FP reduction consists of feature mining from the ROIs using proposed local sparse curvelet coefficients followed by classification using artificial neural network (ANN). The performance of proposed algorithm has been validated using the local datasets as TMCH (Tata Memorial Cancer Hospital) and publicly available MIAS (Suckling et al., 1994) and DDSM (Heath et al., 2000) database. The proposed technique results in reduction of FPs from 0.85 to 0.02 FP/image for MIAS, 4.81 to 0.16 FP/image for DDSM, and 2.32 to 0.05 FP/image for TMCH reflecting huge improvement in classification of mammograms.

Contrast Enhancement of Mammograms for Rapid Detection of Microcalcification Clusters

Introduction Breast cancer is one of the most common types of cancer among women. Early detection of breast cancer is the key to reducing the associated mortality rate. The presence of microcalcifications clusters (MCCs) is one of the earliest signs of breast cancer. Due to poor imaging contrast of mammograms and noise contamination, radiologists may overlook some diagnostic signs, specially the presence of MCCs. In order to improve cancer detection, image enhancement methods are often used to aid radiologists. In this paper, a new enhancement method was presented for the accurate and early detection of MCCs in mammograms. Materials and Methods The proposed system consisted of four main steps including: 1) image scaling;2) breast region segmentation;3) noise cancellation using a filter, which is sensitive to MCCs; and 4) contrast enhancement of mammograms using Contrast-Limited Adaptive Histogram Equalization (CLAHE) and wavelet transform. To evaluate this method, 120 clinical mammograms were used. Results To evaluate the performance of the image enhancement algorithm, contrast improvement index (CII) was used. The proposed enhancement method in this research achieved the highest CII in comparison with other methods applied in this study. The Validity of the results was confirmed by an expert radiologist through visual inspection. Conclusion Detection of MCCs significantly improved in contrast-enhanced mammograms. The proposed method could be helpful for radiologists to easily detect MCCs; it could also decrease the number of biopsies and reduce the frequency of clinical misdiagnosis. Moreover, it could be useful prior to segmentation or classification stages.

Application of a wavelet thresholding algorithm for contrast enhancement of mammograms

The current study presents a novel algorithm for enhancement and visibility of low contrast features in breast mammograms. The contrast of the mammograms is enhanced by the use of mathematical morhphology and wavelet denoising with an emphasis on differentiating edge pixels from noise to enhance visible diagnostic signs like masses and micro-calcification. We present results of our proposed method and compare it with 3 other contrast enhancement algorithms based on quantitative metrics of contrast improvement index (CII) and edge preservation index (EPI), and radiological evaluation. Our results based on these metrics indicate that our method outperforms the 3 other approaches compared. The results of the clinical evaluation are in good agreement with the comparative analyses based on CII and EPI.

Histogram-modified local contrast enhancement for mammogram images

Early detection of breast cancer in the mammograms is essential in the field of medicine. Contrast enhancement of mammograms based on Histogram Equalisation (HE) is presented. The standard HE usually results in excessive contrast enhancement because of the lack of control on the level of enhancement. The Histogram-Modified Local Contrast Enhancement (HMLCE) is introduced in this paper to adjust the level of contrast enhancement, which in turn gives the resultant image a strong contrast and brings the local details for more relevant interpretation. It incorporates both histogram modifications as an optimisation technique and an LCE technique. This method is tested for Mias mammogram images. The performance of this method is determined using three parameters like Enhancement Measure (EME), Absolute Mean Brightness Error (AMBE) and Discrete Entropy (H). From these measures, the proposed technique provides better contrast enhancement while preserving the local information of the mammogram images.

Histogram Modified Local Contrast Enhancement for mammogram images

Early detection of breast cancer in the mammograms is very essential in the field of medicine. Contrast enhancement of mammograms based on Histogram Equalization (HE) is presented. Histogram equalization is an effective and simple technique for contrast enhancement. The standard histogram equalization (HE) usually results in excessive contrast enhancement because of lack of control on the level of enhancement. The Histogram Modified Local Contrast Enhancement (HM-LCE) is introduced in this paper to adjust the level of contrast enhancement, which in turn gives the resultant image a strong contrast and also brings the local details present in the original image for more relevant interpretation. It incorporates a two stage processing both histogram modifications as an optimization technique and a local contrast enhancement technique. This method is tested for Mias mammogram images. The performance of this method is determined using three parameters like Enhancement Measure (EME), Absolute Mean Brightness Error (AMBE) and Discrete Entropy (H) for all 22 numbers of Mias mammogram images with microcalcification. It's enhancement potential is also tested by sobel and otsu methods for the detection of microcalcification in the mammogram image. From the subjective and quantitative measures it is interesting that this proposed technique provides optimum results by giving better contrast enhancement and preserving the local information of the original mammogram images in the Mias data base and the method has increased the detectability of micro calcifications present in the given mammogram image.

A novel vague set approach for selective contrast enhancement of mammograms using multiresolution

The proposed algorithm introduces a novel vague set approach to develop a selective but robust, flexible and intelligent contrast enhancement technique for mammograms. Wavelet based filtering analysis can produce Low Frequency (LF) and High Frequency (HF) subbands of the original input images. The extremely small size microcalcifications become visible under multiresolution techniques. LF subband is then fuzzified by conventional fuzzy c-means clustering (FCM) algorithm with justified number of clusters. HF components, representing the narrow protrusions and other fine details are also fuzzified by FCM with justified number of clusters. Vague set approach captures the hesitancies and uncertainties of truly affected masses/other breast abnormalities with normal glandular tissues. After highlighting the masses/microcalcifications accurately, both LF and HF subbands are transformed back to the original resolution by inverse wavelet transform. The results show that the proposed method can successfully enhance the selected regions of mammograms and provide better contrast images for visual interpretation.

SU‐EE‐A2‐04: Tone Mapping Operators for Contrast Enhancement of Mammograms

Purpose: To investigate the effect of different tone mapping operators in contrast enhancement of mammograms. A concave piecewise linear operator is proposed and is compared to a convex (logarithmic) and linear operator. Method and Materials: Tone mapping is performed prior to contrast enhancement to elevate the lower intensities that mainly correspond to the skin‐line zone. However, this leads to a loss in contrast due to compression of the gray levels. Gaussian band‐pass filtering is then performed to enhance the contrast of the mammogram. Filtering highlights the structure of breast while suppressing noise and the slowly varying high‐density structure. The filtering is done in the frequency domain and the image is mirrored and tiled prior to taking its Fourier transform to minimize edge effects. The tone mapping operators studied are logarithmic, linear and piecewise linear. The logarithmic operator has an adaptive base, the linear operator has constant slope in the intensities corresponding to the skin‐line zone, and the piecewise linear operator has varying slope in those intensities. Results: The enhanced images were viewed on a calibrated Dome C5i display. The logarithmic operator compresses the intensities near the threshold excessively which results in substantial loss of information. The linear and piecewise linear operators do not compress to the same extent. The piecewise linear operator compresses more in the range where there is not much information than in the range where there is more information. Also, tone mapping decreases the magnitude of the intensity step‐change from the breast region to the background. This reduces the ringing artifacts that result from the filtering operation Conclusion: The tone mapping operation improved the visual characteristics of the mammograms. Piecewise linear operator performed the best. The linear operator is comparable to the piecewise linear operator in most cases, though not as consistent. The logarithmic operator fared poorly.

Improvement of sensitivity of breast cancer diagnosis with adaptive neighborhood contrast enhancement of mammograms.

Mammograms are difficult to interpret, especially of cancers at their early stages. In this paper, we analyze the effectiveness of our adaptive neighborhood contrast enhancement (ANCE) technique in increasing the sensitivity of breast cancer diagnosis. Seventy-eight screen-film mammograms of 21 difficult cases (14 benign and seven malignant), 222 screen-film mammograms of 28 interval cancer patients and six benign control cases were digitized with a high-resolution of about 4096 x 2048 x 10-bit pixels and then processed with the ANCE method. Unprocessed and processed digitized mammograms as well as the original films were presented to six experienced radiologists for a receiver operating characteristic (ROC) evaluation for the difficult case set and to three reference radiologists for the interval cancer set. The results show that the radiologists' performance with the ANCE-processed images is the best among the three sets of images (original, digitized, and enhanced) in terms of area under the ROC curve and that diagnostic sensitivity is improved by the ANCE algorithm. All of the 19 interval cancer cases not detected with the original films of earlier mammographic examinations were diagnosed as malignant with the corresponding ANCE-processed versions, while only one of the six benign cases initially labeled correctly with the original mammograms was interpreted as malignant after enhancement. McNemar's tests of symmetry indicated that the diagnostic confidence for the interval cancer cases was improved by the ANCE procedure with a high level of statistical significance (p-values of 0.0001-0.005) and with no significant effect on the diagnosis of the benign control cases (p-values of 0.08-0.1). This study demonstrates the potential for improvement of diagnostic performance in early detection of breast cancer with digital image enhancement.

Region-based contrast enhancement of mammograms

Diagnostic features in mammograms vary widely in size and shape. Classical image enhancement techniques cannot adapt to the varying characteristics of such features. An adaptive method for enhancing the contrast of mammographic features of varying size and shape is presented. The method uses each pixel in the image as a seed to grow a region. The extent and shape of the region adapt to local image gray-level variations, corresponding to an image feature. The contrast of each region is calculated with respect to its individual background. Contrast is then enhanced by applying an empirical transformation based on each region's seed pixel value, its contrast, and its background. A quantitative measure of image contrast improvement is also defined based on a histogram of region contrast and used for comparison of results. Using mammogram images digitized at high resolution (less than 0.1 mm pixel size), it is shown that the validity of microcalcification clusters and anatomic details is considerably improved in the processed images.