Segmentation of Breast Tissues in Infrared Images Using Modified Phase Based Level Sets
16 Sep 2013-pp 161-174
TL;DR: The results show that the proposed level set method is able to extract the breast tissues in infrared images and able to address the inherent limitations in thermograms such as low contrast and absence of clear edges.
Abstract: In this study, segmentation of frontal breast tissues in infrared thermography is proposed using modified phase based level set method. The images considered for this work are obtained from open source database PROENG. An improved diffusion rate model is adopted and incorporated in distance regularized level set framework. Local phase information is used as an edge indicator for the evolution of level set function. Region based statistics and overlap measures are computed to compare and validate the segmented region of interests against ground truths. . Further, the obtained values are compared with the reported numerical values of three segmentation methods. The results show that the proposed level set method is able to extract the breast tissues in infrared images and able to address the inherent limitations in thermograms such as low contrast and absence of clear edges. A high amount of correlation between the segmented output and ground truths is observed. The performance of the proposed segmentation method is better when compared to reported segmentation methods. The adopted method seems to be effective in identifying the lower breast boundary and inflammatory folds present in breast thermograms.
Citations
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11 Oct 2015
TL;DR: An automatic segmentation method for the Region of Interest (ROI) from breast thermograms is proposed based on the data acquisition protocol parameter and the image statistics of DMR-IR database.
Abstract: Breast cancer is one from various diseases that has got great attention in the last decades. This due to the number of women who died because of this disease. Segmentation is always an important step in developing a CAD system. This paper proposed an automatic segmentation method for the Region of Interest (ROI) from breast thermograms. This method is based on the data acquisition protocol parameter (the distance from the patient to the camera) and the image statistics of DMR-IR database. To evaluated the results of this method, an approach for the detection of breast abnormalities of thermograms was also proposed. Statistical and texture features from the segmented ROI were extracted and the SVM with its kernel function was used to detect the normal and abnormal breasts based on these features. The experimental results, using the benchmark database, DMR-IR, shown that the classification accuracy reached (100%). Also, using the measurements of the recall and the precision, the classification results reached 100%. This means that the proposed segmentation method is a promising technique for extracting the ROI of breast thermograms.
52 citations
Cites background or methods from "Segmentation of Breast Tissues in I..."
...These efforts can be classified into two classes: automatic segmentation of breast regions [7]] [8] and classification based on the asymmetry analysis to normal and abnormal cases [16], [17], [18]....
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...For the automatic segmentation approaches [7], [8], the level set technique [9] used to extract the blood vessels in a thermal image....
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...For the automatic segmentation [7], [8], the level set technique [9] has been used to extract the blood vessels in a thermal image....
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...Another automatic segmentation approach has been proposed in [8] to segment the frontal breast tissues from breast...
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TL;DR: A breast cancer risk index (BCRI) is developed using significant KLPP features which can discriminate the two classes using a single integrated index and can help the radiologists to discriminate the normal and malignant classes during screening to validate their findings.
Abstract: Breast cancer is one of the prime causes of death in women worldwide. Thermography has shown a great potential in screening the breast cancer and overcomes the limitations of mammography. Moreover, interpretations of thermogram images are dependent on the specialists, which may lead to errors and uneven results. Preliminary screening method should detect the hazardous, destructive tumours effectively to improve the accuracy. The growth of malignant tumour can increase the internal temperature which can be captured by thermograms. Thus in this work, locally normalised histogram of oriented gradients (HOG) based preliminary screening computer aided diagnosis tool is proposed. HOG is able to record the minute internal variations in thermograms. In order to reduce the dimensions of extracted HOG descriptors kernel locality preserving projection (KLPP) is used. The resulting KLPP features are then ranked to form an efficient classification model. Various machine learning algorithms are used to validate...
45 citations
05 Nov 2015
TL;DR: The experimental results showed that the proposed CAD system classifying breast cancer thermograms to normal and abnormal is a very promising step toward automatic diagnosis of breast cancer using thermograms as the accuracy reached 100%.
Abstract: The early detection of breast cancer makes many women survive. In this paper, a CAD system classifying breast cancer thermograms to normal and abnormal is proposed. This approach consists of two main phases: automatic segmentation and classification. For the former phase, an improved segmentation approach based on both Neutrosophic sets (NS) and optimized Fast Fuzzy c-mean (F-FCM) algorithm was proposed. Also, post-segmentation process was suggested to segment breast parenchyma (i.e. ROI) from thermogram images. For the classification, different kernel functions of the Support Vector Machine (SVM) were used to classify breast parenchyma into normal or abnormal cases. Using benchmark database, the proposed CAD system was evaluated based on precision, recall, and accuracy as well as a comparison with related work. The experimental results showed that our system would be a very promising step toward automatic diagnosis of breast cancer using thermograms as the accuracy reached 100%.
42 citations
Cites background or methods from "Segmentation of Breast Tissues in I..."
...For the automatic segmentation [8], [15], the level set technique [19] has been used to extract the blood vessels in a thermal image....
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...These efforts can be classified into: automatic segmentation of breast regions [8], [15] and classification based on the asymmetry analysis to normal and...
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...Another automatic segmentation approach has been proposed in [15] to segment the frontal breast tissues from breast thermograms....
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30 Sep 2018
TL;DR: It is concluded that infrared thermography with the help of an adequate automatic classification algorithm can be a valuable and reliable complementary tool for radiologist in detecting breast cancer and thereby helping to reduce mortality rates.
Abstract: Breast cancer is one of the most common women cancers in the world. In this paper, a new approach based on thermography for the early detection of breast abnormality is proposed. The study involved 80 breast thermograms collected from the PROENG public database which consists of 50 healthy breasts and 30 with some findings. Image processing techniques such as segmentation, texture analysis and mathematical morphology were used to train a support vector machine (SVM) classifier for automatic detection of breast abnormality. After conducting several tests, we obtained very interesting and motivating results. Indeed, our method showed a high performance in terms of sensitivity of 93.3%, a specificity of 90% and an accuracy of 91.25%. The final results let us conclude that infrared thermography with the help of an adequate automatic classification algorithm can be a valuable and reliable complementary tool for radiologist in detecting breast cancer and thereby helping to reduce mortality rates.
16 citations
Cites background from "Segmentation of Breast Tissues in I..."
...265 [22] Srinivasan, Suganthi Salem, and Ramakrishnan Swaminathan....
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...Various segmentation algorithms have b een proposed to delineate the breast automatically or semi-automatically, but wit h moderate success rates [20][21][22][23]....
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21 Sep 2016
TL;DR: Analysis of breast thermograms based on segmentation of region of interest which is extracted as hot region followed by colour analysis and the results are compared with doctor’s diagnosis to confirm that infra-red thermography is a reliable diagnostic tool in breast cancer identification.
Abstract: Breast cancer is the commonly found cancer in women. Studies show that the detection at the earliest can bring down the mortality rate. Infrared Breast thermography uses the temperature changes in breast to arrive at diagnosis. Due to increased cell activity, the tumor and the surrounding areas has higher temperature emitting higher infrared radiations. These radiations are captured by thermal camera and indicated in pseudo colored image. Each colour of thermogram is related to specific range of temperature. The breast thermogram interpretation is primarily based on colour analysis and asymmetry analysis of thermograms visually and subjectively. This study presents analysis of breast thermograms based on segmentation of region of interest which is extracted as hot region followed by colour analysis. The area and contours of the hottest regions in the breast images are used to indicate abnormalities. These features are further given to ANN classifier for automated analysis. The results are compared with doctor’s diagnosis to confirm that infra-red thermography is a reliable diagnostic tool in breast cancer identification.
16 citations
References
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TL;DR: A new variational level set formulation in which the regularity of the level set function is intrinsically maintained during thelevel set evolution called distance regularized level set evolution (DRLSE), which eliminates the need for reinitialization and thereby avoids its induced numerical errors.
Abstract: Level set methods have been widely used in image processing and computer vision. In conventional level set formulations, the level set function typically develops irregularities during its evolution, which may cause numerical errors and eventually destroy the stability of the evolution. Therefore, a numerical remedy, called reinitialization, is typically applied to periodically replace the degraded level set function with a signed distance function. However, the practice of reinitialization not only raises serious problems as when and how it should be performed, but also affects numerical accuracy in an undesirable way. This paper proposes a new variational level set formulation in which the regularity of the level set function is intrinsically maintained during the level set evolution. The level set evolution is derived as the gradient flow that minimizes an energy functional with a distance regularization term and an external energy that drives the motion of the zero level set toward desired locations. The distance regularization term is defined with a potential function such that the derived level set evolution has a unique forward-and-backward (FAB) diffusion effect, which is able to maintain a desired shape of the level set function, particularly a signed distance profile near the zero level set. This yields a new type of level set evolution called distance regularized level set evolution (DRLSE). The distance regularization effect eliminates the need for reinitialization and thereby avoids its induced numerical errors. In contrast to complicated implementations of conventional level set formulations, a simpler and more efficient finite difference scheme can be used to implement the DRLSE formulation. DRLSE also allows the use of more general and efficient initialization of the level set function. In its numerical implementation, relatively large time steps can be used in the finite difference scheme to reduce the number of iterations, while ensuring sufficient numerical accuracy. To demonstrate the effectiveness of the DRLSE formulation, we apply it to an edge-based active contour model for image segmentation, and provide a simple narrowband implementation to greatly reduce computational cost.
1,947 citations
01 Jan 1995
TL;DR: Videre: Journal of Computer Vision Research is a quarterly journal published electronically on the Internet by The MIT Press, Cambridge, Massachusetts, 02142 and prices subject to change without notice.
Abstract: Videre: Journal of Computer Vision Research (ISSN 1089-2788) is a quarterly journal published electronically on the Internet by The MIT Press, Cambridge, Massachusetts, 02142. Subscriptions and address changes should be addressed to MIT Press Journals, Five Cambridge Center, Cambridge, MA 02142; phone: (617) 253-2889; fax: (617) 577-1545; e-mail: journals-orders@mit.edu. Subscription rates are: Individuals $30.00, Institutions $125.00. Canadians add additional 7% GST. Prices subject to change without notice.
1,186 citations
Patent•
08 Mar 2007TL;DR: In this paper, a steerable catheter is used to steer the distal end of the catheter from its proximal end as it is advanced with the body, so that it can be viewed from the proximal point of view.
Abstract: Several embodiments of the present invention are generally directed to medical visualization systems that comprise combinations of disposable and resuable components, such as catheters, functional handles, hubs, optical devices, etc. Other embodiments of the present invention are generally directed to features and aspects of an in-vivo visualization system that comprises an endoscope having a working channel through which a catheter having viewing capabilities is routed. The catheter may obtain viewing capabilities by being constructed as a vision catheter or by having a fiberscope or other viewing device selectively routed through one of its channels. The catheter is preferably of the steerable type so that the distal end of the catheter may be steered from its proximal end as it is advanced with the body. Some embodiments of the invention are directed to in-vivo visualization devices and systems comprising user-actuatable control features and steering devices. A suitable use for the in-vivo visualization system includes but is not limited to diagnosis and/or treatment of the duodenum, and particularly the biliary tree.
729 citations
TL;DR: The principles are organized around two basic intuitions: first, if a boundary were changed only slightly, then, in general, its shape would change only slightly; and second, that not all contours are shapes, but rather only those that can enclose “physical” material.
Abstract: We undertake to develop a general theory of two-dimensional shape by elucidating several principles which any such theory should meet. The principles are organized around two basic intuitions: first, if a boundary were changed only slightly, then, in general, its shape would change only slightly. This leads us to propose an operational theory of shape based on incremental contour deformations. The second intuition is that not all contours are shapes, but rather only those that can enclose “physical” material. A theory of contour deformation is derived from these principles, based on abstract conservation principles and Hamilton-Jacobi theory. These principles are based on the work of Sethian (1985a, c), the Osher-Sethian (1988), level set formulation the classical shock theory of Lax (1971; 1973), as well as curve evolution theory for a curve evolving as a function of the curvature and the relation to geometric smoothing of Gage-Hamilton-Grayson (1986; 1989). The result is a characterization of the computational elements of shape: deformations, parts, bends, and seeds, which show where to place the components of a shape. The theory unifies many of the diverse aspects of shapes, and leads to a space of shapes (the reaction/diffusion space), which places shapes within a neighborhood of “similar” ones. Such similarity relationships underlie descriptions suitable for recognition.
559 citations