On techniques for detecting circumscribed masses in mammograms

Lai, Shiqi; Li, X.; Biscof, W.F.

doi:10.1109/42.41491

Cited by 206 publications

(89 citation statements)

References 14 publications

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“…This was followed by a classification stage, where the ROI were classified as malignant, benign or normal based on features like shape descriptors, edge descriptors and area. Petrick et al [11] developed a two-stage algorithm for the enhancement of suspicious objects. In the first stage they proposed an adaptive density weighted contrast enhancement filter (DWCE) to enhance objects and suppress background structures.…”

Section: E Existing Research Studymentioning

confidence: 99%

Review on Mammogram Mass Detection by Machine Learning Techniques

Raman¹,

Sumari²,

Then³

et al. 2011

IJCEE

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Abstract-Breast cancer continues to be a significant public health problem in the world and number one cause for death rate in Malaysia. Early detection is the key for improving breast cancer prognosis. Mammography is the most effective tool now available for an early diagnosis of breast cancer. However, the detection of cancer signs in mammograms is a difficult task due to irregular pathological structures and noise which are present in the image. It has been shown that in current breast cancer screenings 8%-20% of the tumors are missed by the radiologists. For this reason, a lot of research is currently being done to develop systems for computer aided detection to improve the accuracy. In this paper, review of mammogram mass detection and segmentation is focused. The main aim of the paper is to summarize and compares the method of mass detection in mammogram images. In specific, preprocessing, segmentation, feature extraction and classifications are discussed, Receiver operating curve and free-response receiver operating curve of each method is highlighted to show the sensitivity and specificity of the tumor detection.

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Section: E Existing Research Studymentioning

confidence: 99%

Review on Mammogram Mass Detection by Machine Learning Techniques

Raman¹,

Sumari²,

Then³

et al. 2011

IJCEE

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show abstract

“…Several papers on model-based methods are mentioned below. The early paper was written by Lai et al [5]. Constantinidis et al [6,7] used normalized cross-correlation for detection of cancerous masses.…”

Section: Introductionmentioning

confidence: 99%

“…In pattern or template matching, the training usually includes a presentation of a collection of images containing the object to detect. Various modifications of templates were proposed, beginning with the paper by Lai et al [5]. In that paper, tests are made on 17 mammograms containing circumscribed masses.…”

Section: Introductionmentioning

confidence: 99%

Detection of Cancerous Masses in Mammograms by Template Matching: Optimization of Template Brightness Distribution by Means of Evolutionary Algorithm

Bator

Nieniewski

2011

J Digit Imaging

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Optimization of brightness distribution in the template used for detection of cancerous masses in mammograms by means of correlation coefficient is presented. This optimization is performed by the evolutionary algorithm using an auxiliary mass classifier. Brightness along the radius of the circularly symmetric template is coded indirectly by its second derivative. The fitness function is defined as the area under curve (AUC) of the receiver operating characteristic (ROC) for the mass classifier. The ROC and AUC are obtained for a teaching set of regions of interest (ROIs), for which it is known whether a ROI is true-positive (TP) or false-positive (F). The teaching set is obtained by running the mass detector using a template with a predetermined brightness. Subsequently, the evolutionary algorithm optimizes the template by classifying masses in the teaching set. The optimal template (OT) can be used for detection of masses in mammograms with unknown ROIs. The approach was tested on the training and testing sets of the Digital Database for Screening Mammography (DDSM). The free-response receiver operating characteristic (FROC) obtained with the new mass detector seems superior to the FROC for the hemispherical template (HT). Exemplary results are the following: in the case of the training set in the DDSM, the true-positive fraction (TPF)=0.82 for the OT and 0.79 for the HT; in the case of the testing set, TPF=0.79 for the OT and 0.72 for the HT. These values were obtained for disease cases, and the false-positive per image (FPI)=2.

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“…A number of image processing methods have been proposed to perform this task. S. M. Lai et al [5] and W. Qian et al [6] have proposed using modified and weighted median filtering, respectively, to enhance the digitized image prior to object identification. D. Brzakovic et all [7] used thresholding and fuzzy pyramid linking for mass localization and classification.…”

Section: Introductionmentioning

confidence: 99%

Tumor Demarcation in Mammography Images using LBG on Probability Image

Kekre¹,

Gharge²,

Sarode³

2010

IJCA

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The ability to improve diagnostic information from medical images can be enhanced by designing computer processing algorithms that is why we proposed new algorithm to detect cancer in mammogram breast cancer images. In this paper we proposed segmentation using vector quantization technique. Here we used Linde Buzo and Gray (LBG)for segmentation of mammographic images on probability image. Initially probability of input image is calculated and displayed as a result. In second step a codebook of size 128 was generated for probability image. These code vectors were further reclustered in 8 clusters using same LBG algorithm. These 8 images were displayed as a result. This approach does not leads to over segmentation or under segmentation. For the comparison purpose we displayed results of GLCM and watershed segmentation along with this method. General TermsAlgorithms

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On techniques for detecting circumscribed masses in mammograms

Cited by 206 publications

References 14 publications

Review on Mammogram Mass Detection by Machine Learning Techniques

Review on Mammogram Mass Detection by Machine Learning Techniques

Detection of Cancerous Masses in Mammograms by Template Matching: Optimization of Template Brightness Distribution by Means of Evolutionary Algorithm

Tumor Demarcation in Mammography Images using LBG on Probability Image

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