Accurate Breast Contour Detection Algorithms in Digital Mammogram

Maitra, Indra Kanta; Nag, Sanjay; Bandyopadhyay, Samir Kumar

doi:10.5120/3031-4109

Cited by 14 publications

(10 citation statements)

References 24 publications

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“…[15][16][17][18][20][21][22][23][24][25][26][27][28] Also, by using this technique with the help of advanced digital image processing, we would be able to distinguish and differentiate between different grade levels of tumor and cancerous cells. The novelty of the current work lies in several areas as mentioned in this scenario; first, on the imaging front, we have been able to achieve high-resolution photothermal imaging of tumor with size less than 2 cm 3 using NIR laser.…”

Section: Discussionmentioning

confidence: 99%

“…20,21,28,29 The edges identified by edge detection are often disconnected. Region boundaries and edges are closely related, since there is often a sharp adjustment in intensity at the region boundaries.…”

Section: Image Processingmentioning

confidence: 99%

“…22,23,28,29 It is robust to noise and implements a Gaussian function to smooth the image and to obtain the magnitude and orientation of the gradient for each pixel. 22,23,28,29 It is robust to noise and implements a Gaussian function to smooth the image and to obtain the magnitude and orientation of the gradient for each pixel.…”

Section: Image Processingmentioning

confidence: 99%

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High-performance near-infrared imaging for breast cancer detection

El-Sharkawy

El-Sherif²

2014

J. Biomed. Opt

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We present a method for the noninvasive determination of the size, position, and optical properties of tumors in the human breast. The tumor is first detected by photothermal imaging. It is then sized, located, and optically characterized using designed digital image processing and edge-detection pattern recognition. The method assumes that the tumor is spherical and inhomogeneous and embedded in an otherwise homogeneous tissue. Heat energy is deposited in the tissue by absorption of near-infrared (NIR) Nd:YAG laser radiation, and its subsequent conversion to heat via vibrational relaxation causes a rise in temperature of the tissue. The tumor absorbs and scatters NIR light more strongly than the surrounding healthy tissue. Heat will diffuse through the tissue, causing a rise in temperature of the surrounding tissue. Differentiation between normal and cancerous tissues is determined using IR thermal imaging. Results are presented on a 55-year-old patient with a papillary breast cancer. We found that these results provide the clinician with more detailed information about breast lesions detected by photothermal imaging and thereby enhance its potential for specificity.

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Section: Discussionmentioning

confidence: 99%

Section: Image Processingmentioning

confidence: 99%

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High-performance near-infrared imaging for breast cancer detection

El-Sharkawy

El-Sherif²

2014

J. Biomed. Opt

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“…Breast shape is one of the biggest single highlights of mammogram. Indra Kanta Maitra et al [11] proposed automated segment algorithm and boundary detection method to extract breast region. Three methods are combined to segment the breast area.…”

Section: Mp Sukassini Tvelmuruganmentioning

confidence: 99%

Ascertaining Abnormal Regions in Mammogram Images using Gravitational Search Local Map View Technique

Sukassini¹,

Velmurugan²

2019

IJITEE

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Segmentation of mammogram images has gained importance in many medical treatments and diagnostic processes. Mammogram image segmentation aims at correctly separating different tissues, organs, or pathologies in volumetric image data. Most of the existing algorithms for image segmentation have a "scattered" cluster problem (disconnected clusters) happened in many clustering techniques (agglomerative, k-means, Dbscan) . Above algorithms not taken into account of both quality value and connectivity of points and region varying shapes. Two methods are proposed in this paper. The first technique is LMV (Local Map View) and the second technique is GSLMV (Gravitational Search Local Map View). LMV concentrates on determination of local quality for each point in all instances of the region in the comparative similarity view by applying the initial cluster technique. This view allows the user to choose instances for detailed analysis and filter the outlier instances from the input, next specific feature selection process identifies regions with systematic characteristics across the images. In this research work, pixels in groups with high intensity are assumed to be abnormal regions in cancer and non-cancer images. Fuzzy clustering is used to cluster the pixels. The optimal threshold from GS are initialized as cluster centres. This increases the speed of GSLMV algorithm. Performances of GSA, LMV and GSLMV methods are measured using False Rejection Rate, pixel count, Peak signal to noise ratio and runtime metrics. GSLMV showed better results based on pixel count, PSNR and runtime.

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“…The important part on an image is the breast outskirt, also called the skin-air interface, or bosom limit. The breast form can be acquired by compression in the [15] imageinto breast and non-breast areas. The extracted breast contour should adequately model the soft tissue/air interface and preserve the nipple in profile.…”

Section: Introductionmentioning

confidence: 99%

Image Compression Using Local Map View Technique

Rajeswari¹,

S.²

2021

INDJCSE

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Image compression has gained importance in many medical treatments and diagnostic processes. Image compression aims at correctly separating different tissues, organs, or pathologies in volumetric image data. Most of the existing algorithms for Image compression have a "scattered" cluster problem (disconnected clusters) happened in many clustering techniques (agglomerative, k-means, Dbscan) above algorithms not taken into account of both quality value and connectivity of points and region varying shapes. This proposed technique LMV (Local Map View) concentrates on determination of local quality for each point in all instances of the region in the comparative similarity view by applying the initial cluster technique. This view allows the user to choose instances for detailed analysis and filter the outlier instances from the input, next specific feature selection process identifies regions with systematic characteristics across the images. Then the process continues showing threshold marked of the regions and displays the Image compression quality across selected instances for standard image values as resultant dataset. This proposed technique shown the better results in case Image compression constraints (multi-level threshold, MSE, FRR) by the existing techniques.

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Accurate Breast Contour Detection Algorithms in Digital Mammogram

Cited by 14 publications

References 24 publications

High-performance near-infrared imaging for breast cancer detection

High-performance near-infrared imaging for breast cancer detection

Ascertaining Abnormal Regions in Mammogram Images using Gravitational Search Local Map View Technique

Image Compression Using Local Map View Technique

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