Image Processing Algorithms for Digital Mammography: A Pictorial Essay

Pisano, Etta D.; Cole, Elodia B.; Hemminger, Bradley M.; Yaffe, Martin J.; Aylward, Stephen; Maidment, Andrew D. A.; Johnston, Richard; Williams, Mark B.; Niklason, Loren T.; Conant, Emily F.; Fajardo, Laurie L.; Kopans, Daniel B.; Brown, Marylee E.; Pizer, Stephen M.

doi:10.1148/radiographics.20.5.g00se311479

Cited by 140 publications

(81 citation statements)

References 6 publications

(4 reference statements)

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“…In this work, we enhance the original mammograms while maximizing the preservation of their inherent characteristics. Pisano et al [8] examined several digital mammograms using multiple methods of enhancement, including CLAHE. They concluded that image detail is good and that, in general, lesions appeared obvious compared to the background.…”

Section: Related Workmentioning

confidence: 99%

“…The advantage of CLAHE is that it is straightforward to implement and results in a high contrast image. As per the conclusions of Pisano et al on the efficacy of CLAHE [8], the original mammographic images are first processed using CLAHE to eliminate noise and followed by the application of a nonlinear filter, which incorporates the advantages of a nonlinear fuzzy function.…”

Section: Related Workmentioning

confidence: 99%

“…This work is inspired by CLAHE [8], with modifications to improve its performance. Pisano et al [8] examined several image-enhancement methods, and expressed that CLAHE "might be helpful in allowing radiologists to see subtle edge information, such as spiculations".…”

Section: Preprocessing Filter: Fclahementioning

confidence: 99%

“…The major advantage of this filter compared to earlier approaches [6][7][8][9] is its ability to eliminate noise without affecting the intensity characteristics of mammographic image. One of the most common approaches to segment mammograms is by utilizing the statistical distribution of their intensities.…”

Section: F(d) Is the Fuzzy Function Defined In (1) ( ) Is The Origmentioning

confidence: 99%

“…Previous works on mammographic-image preprocessing used methods such as Gamma correction [6], adaptive 2D Wiener filtering [7], contrast-limited adaptive histogram equalization (CLAHE) [8], and multi-scale wavelet-based enhancement [9]. It is a difficult task to choose the single best technique for image enhancement, and the enhancement most often is evaluated based on the performance of the subsequent segmentation algorithm [10].…”

Section: Introductionmentioning

confidence: 99%

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A New Preprocessing Filter for Digital Mammograms

Rahmati

Hamarneh

Nussbaum

et al. 2010

Lecture Notes in Computer Science

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Abstract. 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.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Preprocessing Filter: Fclahementioning

confidence: 99%

Section: F(d) Is the Fuzzy Function Defined In (1) ( ) Is The Origmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A New Preprocessing Filter for Digital Mammograms

Rahmati

Hamarneh

Nussbaum

et al. 2010

Lecture Notes in Computer Science

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Performance comparison of texture feature analysis methods using PNN classifier for segmentation and classification of brain CT images

Padma¹,

Giridharan

2016

Int. J. Imaging Syst. Technol.

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A computer software system is designed for the segmentation and classification of benign and malignant tumor slices in brain computed tomography images. In this paper, we present a texture analysis methods to find and select the texture features of the tumor region of each slice to be segmented by support vector machine (SVM). The images considered for this study belongs to 208 benign and malignant tumor slices. The features are extracted and selected using Student's t-test. The reduced optimal features are used to model and train the probabilistic neural network (PNN) classifier and the classification accuracy is evaluated using k fold cross validation method. The segmentation results are also compared with the experienced radiologist ground truth. Quantitative analysis between ground truth and segmented tumor is presented in terms of quantitative measure of segmentation accuracy and the overlap similarity measure of Jaccard index. The proposed system provides some newly found texture features have important contribution in segmenting and classifying benign and malignant tumor slices efficiently and accurately. The experimental results show that the proposed hybrid texture feature analysis method using Probabilistic Neural Network (PNN) based classifier is able to achieve high segmentation and classification accuracy effectiveness as measured by Jaccard index, sensitivity, and specificity.

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