&lt;title&gt;Lesion detection and characterization in digital mammography by Bezier histograms&lt;/title&gt;

Qi, Hairong; Snyder, Wesley E.

doi:10.1117/12.348554

Cited by 4 publications

(3 citation statements)

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“…There have been studies on this challenging problem using sophisticated computer vision algorithms: texturebased [5], wavelet-based [6], Markov random field-based [7], fractal-based [8], etc. Even though they showed good performances for some cases, no single model can be used as a general processing model due to the variety of breast tissues and lesions [9]. We proposed a multi-stage ROI detection method that combines image processing techniques and artificial neural networks to accommodate the varieties of tissues and lesions.…”

Section: Introductionmentioning

confidence: 99%

<title>Detection of microcalcifications ROI in digital mammograms using two stages of neural networks</title>

2001

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In this paper, we present an efficient algorithm to detect microcalcifications ROI (Regions of Interest) in digital mammograms using two stages of neural networks. To efficiently detect microcalcifications ROI, we used four sequential processes; preprocessing for breast area detection, modified multilevel thresholding, ROI selection using simple thresholding filters and final ROI selection with two stages of neural networks. In modified multilevel thresholding, the shape property of microcalcification resulted from the gray-level difference with surroundings is used. This algorithm separates microcalcifications from tissues by applying the half-toning technique for different gray-levels. The first selection process with simple thresholding filters defines the filter parameters using the statistically extracted shape property and then it eliminates tissues, which are obviously recognized, to reduce the processing overhead in the next step. The final selection process using neural networks is to detect the ROI in two steps. Through the two stages of neural networks, ROIs with microcalcifications are selected. Each neural network compares and analyzes recognition performance after training. The ROI detection method for microcalcification used in this paper is the first stage for a CAD system. The designed ROI detection methods efficiently find 98.06% of with microcalcifications.

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Section: Introductionmentioning

confidence: 99%

<title>Detection of microcalcifications ROI in digital mammograms using two stages of neural networks</title>

2001

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“…The breast cancer prevention is impossible presently; data indicates declining death rates [2] due to mainly cancer detection where mammography has a big role to play. Widespread mammography use needs aids from computer technology.…”

Section: Introductionmentioning

confidence: 99%

Computer A ided Detection of Tumours in Mammograms

Ramani¹,

Vanitha²

2014

IJIGSP

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Mammography is a special CT scan technique, which uses X-rays and high-resolution film to detect breast tumors efficiently. Mammography is used only in breast tumor detection, and images help physicians to detect diseases due to cells normal growth. Mammography is an effective imaging modality for early breast cancer abnormalities detection. Computer aided diagnosis helps the radiologists to detect abnormalities earlier than traditional procedures. In this paper, an automated mammogram classification method is presented. Symlet, singular value decomposition and weighted histograms are used for feature extraction in mammograms. The extracted features are classified using naï ve bayes, random forest and neural network algorithms.

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“…With this image, the thresholds of the regions bounded by the edges are extracted. In [ 17 ], Qi and Snyder proposed a method for mass segmentation. They used B'ezier splines to interpolate histograms, from which they extracted the region with threshold values at local maxima.…”

Section: Introductionmentioning

confidence: 99%

Mass segmentation using a combined method for cancer detection

et al. 2011

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BackgroundBreast cancer is one of the leading causes of cancer death for women all over the world and mammography is thought of as one of the main tools for early detection of breast cancer. In order to detect the breast cancer, computer aided technology has been introduced. In computer aided cancer detection, the detection and segmentation of mass are very important. The shape of mass can be used as one of the factors to determine whether the mass is malignant or benign. However, many of the current methods are semi-automatic. In this paper, we investigate fully automatic segmentation method.ResultsIn this paper, a new mass segmentation algorithm is proposed. In the proposed algorithm, a fully automatic marker-controlled watershed transform is proposed to segment the mass region roughly, and then a level set is used to refine the segmentation. For over-segmentation caused by watershed, we also investigated different noise reduction technologies. Images from DDSM were used in the experiments and the results show that the new algorithm can improve the accuracy of mass segmentation.ConclusionsThe new algorithm combines the advantages of both methods. The combination of the watershed based segmentation and level set method can improve the efficiency of the segmentation. Besides, the introduction of noise reduction technologies can reduce over-segmentation.

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<title>Lesion detection and characterization in digital mammography by Bezier histograms</title>

Cited by 4 publications

References 1 publication

<title>Detection of microcalcifications ROI in digital mammograms using two stages of neural networks</title>

<title>Detection of microcalcifications ROI in digital mammograms using two stages of neural networks</title>

Computer A ided Detection of Tumours in Mammograms

Mass segmentation using a combined method for cancer detection

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