Improvement of microcalcification cluster detection in mammography utilizing image enhancement techniques

Papadopoulos, Aristotelis-Angelos; Fotiadis, D.I.; Costaridou, Lena

doi:10.1016/j.compbiomed.2008.07.006

Cited by 114 publications

(71 citation statements)

References 40 publications

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“…In the preprocessing module, the significant features of the mammogram are enhanced, recovering most of the hidden characteristics and improving the image quality. According to recent findings (Papadopoulos, 2008), the contribution of the preprocessing module in the detection of ability of the CAD system is definite. Consequently, the final outcome of the CAD scheme depends largely on the pre-processing steps.…”

Section: Mammograms Enhancementmentioning

confidence: 99%

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Microcalcification Detection in Digitized Mammograms: A Neurobiologically-Inspired Approach

Ramírez-Villegas¹,

Ramirez-Moreno²

2012

Digital Image Processing

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Section: Mammograms Enhancementmentioning

confidence: 99%

“…Enhancement algorithms have been employed for the improvement of contrast features and the suppression of noise (Papadopoulos, 2008). They are commonly used to increase the radiologist's detection effectiveness or as pre-processing stages of CAD schemes.…”

Section: Mammograms Enhancementmentioning

confidence: 99%

Microcalcification Detection in Digitized Mammograms: A Neurobiologically-Inspired Approach

Ramírez-Villegas¹,

Ramirez-Moreno²

2012

Digital Image Processing

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“…The contrast in mammograms is very low and the boundary between normal tissue and tumors is unclear, the traditional segmentation methods might not work well (Cheng et al, 2006). Image enhancement algorithm has been utilized for the improvement of contrast features and Science Publications OJBS the suppression of noise (Papadopoulos et al, 2008). Contrast Limited Adaptive Histogram Equalization (CLAHE) based on local parameters was proposed by (Pizer et al, 1987), region based approach for the enhancement of Regions of Interest (ROI) has been proposed by (Morrow et al, 1992).…”

Section: Introductionmentioning

confidence: 99%

“…Successful treatment relies on early detection (Padayachee et al, 2007). Digital mammography is one of the most reliable method involves in the detection and diagnosis of breast pathological disorders (Papadopoulos et al, 2008). Analyzing mammogram images is the most challenging task in medical image processing.…”

Section: Introductionmentioning

confidence: 99%

“…Computer Aided Detection (CAD) tool is the aid for the radiologists in analyzing such images for the effective detection and diagnosis of the disease. Such a CAD tool consists of Preprocessing, Segmentation and detection processes (Papadopoulos et al, 2008). Dense regions in digital mammogram images are usually noisy and have low contrast and their visual screening is difficult (Scharcanski and Jung, 2006).…”

Section: Introductionmentioning

confidence: 99%

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New Computer Aided Detection Method for the Effective Detection of Breast Cancer

Senthilkumar¹

2012

OnLine Journal of Biological Sciences

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Analyzing mammogram images is the most challenging task for radiologists in detecting breast cancer. Computer Aided Detection (CAD) plays major role in detecting such disease. Preprocessing, segmentation and detection are the processes involved in CAD. In this study, we have designed a CAD by improving the processes for the effective detection for breast cancer. Selective median filter has been used for noise reduction, Modified Local Range Modification (MLRM) is used for the enhancement, Cloud Model Based Region Growing Segmentation (CMBRGS) is used for effective segmentation of suspected area and rank based method is used for detection of cancer. This CAD method has been tested for over 40 mammogram images and found the detection accuracy of 98.8%.

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Automatic intensity windowing of mammographic images based on a perceptual metric

2017

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Purpose: Initial auto-adjustment of the window level WL and width WW applied to mammographic images. The proposed intensity windowing (IW) method is based on the maximization of the mutual information (MI) between a perceptual decomposition of the original 12-bit sources and their screen displayed 8-bit version. Besides zoom, color inversion and panning operations, IW is the most commonly performed task in daily screening and has a direct impact on diagnosis and the time involved in the process. Methods: The authors present a human visual system and perception-based algorithm named GRAIL (Gabor-relying adjustment of image levels). GRAIL initially measures a mammogram's quality based on the MI between the original instance and its Gabor-filtered derivations. From this point on, the algorithm performs an automatic intensity windowing process that outputs the WL/WW that best displays each mammogram for screening. GRAIL starts with the default, high contrast, wide dynamic range 12-bit data, and then maximizes the graphical information presented in ordinary 8-bit displays. Tests have been carried out with several mammogram databases. They comprise correlations and an ANOVA analysis with the manual IW levels established by a group of radiologists. A complete MATLAB implementation of GRAIL is available at https://github.com/TheAnswerIsFortyTwo/GRAIL. Results: Auto-leveled images show superior quality both perceptually and objectively compared to their full intensity range and compared to the application of other common methods like global contrast stretching (GCS). The correlations between the human determined intensity values and the ones estimated by our method surpass that of GCS. The ANOVA analysis with the upper intensity thresholds also reveals a similar outcome. GRAIL has also proven to specially perform better with images that contain micro-calcifications and/or foreign X-ray-opaque elements and with healthy BI-RADS A-type mammograms. It can also speed up the initial screening time by a mean of 4.5 s per image. Conclusions: A novel methodology is introduced that enables a quality-driven balancing of the WL/ WW of mammographic images. This correction seeks the representation that maximizes the amount of graphical information contained in each image. The presented technique can contribute to the diagnosis and the overall efficiency of the breast screening session by suggesting, at the beginning, an optimal and customized windowing setting for each mammogram.

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Improvement of microcalcification cluster detection in mammography utilizing image enhancement techniques

Cited by 114 publications

References 40 publications

Microcalcification Detection in Digitized Mammograms: A Neurobiologically-Inspired Approach

Microcalcification Detection in Digitized Mammograms: A Neurobiologically-Inspired Approach

New Computer Aided Detection Method for the Effective Detection of Breast Cancer

Automatic intensity windowing of mammographic images based on a perceptual metric

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