Asymmetry analysis of breast thermograms using BM3D technique and statistical texture features
23 May 2014-pp 1-4
TL;DR: In this paper, the breast tissues are extracted from background tissue by multiplying ground truth masks with denoised images and the midpoint of inframammary folds is identified to separate left and right regions from segmented images.
Abstract: Medical thermography plays a significant role in early detection of breast cancer. The abnormality detection using asymmetry analysis is complex due to low contrast, low signal to noise ratio and absence of clear edges. In this work, asymmetry analysis is carried out on denoised breast thermal images. Block matching and 3D filtering technique (BM3D) is adopted for noise removal. The breast tissues are extracted from background tissue by multiplying ground truth masks with denoised images. The midpoint of inframammary folds is identified to separate left and right regions from segmented images. Normal and abnormal groups are categorized based on the healthy and pathological conditions of the separated breast tissues. Second order features of co-occurrence matrix such as energy, entropy, contrast and difference of variance are extracted from denoised and raw images. Features from denoised images are found to be very effective in discriminating abnormalities present in breast tissues. Hence, it appears that the features extracted from denoised images can be used efficiently to identify the abnormality breast thermograms.
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TL;DR: This study aims at presenting a review that shows the new applications of machine learning and deep learning technology for detecting and classifying breast cancer and provides an overview of progress and the future trends and challenges in the classification and detection of breast cancer.
Abstract: Breast cancer is the second leading cause of death for women, so accurate early detection can help decrease breast cancer mortality rates. Computer-aided detection allows radiologists to detect abnormalities efficiently. Medical images are sources of information relevant to the detection and diagnosis of various diseases and abnormalities. Several modalities allow radiologists to study the internal structure, and these modalities have been met with great interest in several types of research. In some medical fields, each of these modalities is of considerable significance. This study aims at presenting a review that shows the new applications of machine learning and deep learning technology for detecting and classifying breast cancer and provides an overview of progress in this area. This review reflects on the classification of breast cancer utilizing multi-modalities medical imaging. Details are also given on techniques developed to facilitate the classification of tumors, non-tumors, and dense masses in various medical imaging modalities. It first provides an overview of the different approaches to machine learning, then an overview of the different deep learning techniques and specific architectures for the detection and classification of breast cancer. We also provide a brief overview of the different image modalities to give a complete overview of the area. In the same context, this review was performed using a broad variety of research databases as a source of information for access to various field publications. Finally, this review summarizes the future trends and challenges in the classification and detection of breast cancer.
164 citations
TL;DR: The proposed CSSA algorithm achieves fast convergence for the unimodal benchmark functions and outperforms the original SSA algorithm and achieves robustness for the segmentation of different healthy and unhealthy cases images compared to the state-of-the-art methods.
Abstract: Breast cancer is one of the most common types of cancer and early detection can significantly decrease the associated mortality rate. Different kinds of segmentation methods were applied to extract regions of interest from breast cancer images that are necessary to improve the classification. In this paper, a segmentation method for breast cancer from thermal images is introduced based on a proposed Chaotic Salp Swarm Algorithm (CSSA). Although the Salp Swarm Algorithm (SSA) shows superiority in single-objective optimization problems, it suffers from a low convergence rate and local optima stagnation. In the proposed method, a segmentation algorithm is formulated using the quick-shift method for superpixels extraction whose parameters are optimized by CSSA. The quick-shift method generates compact and nearly uniform superpixels by clustering the breast thermal image pixels. CSSA algorithm is developed based on ten chaotic maps to enhance the original SSA convergence rate while accuracy could be improved by controlling the balance between exploration and exploitation. The proposed algorithm is applied to real-world thermal images for the breast area. The results demonstrate that the proposed CSSA algorithm achieves fast convergence for the unimodal benchmark functions and outperforms the original SSA algorithm. Moreover, a dataset from Mastology Research with Infrared Image (DMR-IR) is used to test the performance of the proposed algorithm. In experiments, the proposed optimized segmentation algorithm extracts the breast area from the background accurately where the region of interest is focused on the breast area and removes the unwanted area such as underarms and stomach which intern can enhance the results of cancer detection. Furthermore, the proposed algorithms achieve robustness for the segmentation of different healthy and unhealthy cases images compared to the state-of-the-art methods.
53 citations
Cites background or methods from "Asymmetry analysis of breast thermo..."
...A dataset fromMastology Research with Infrared Image (DMR-IR) is used to test the performance of the proposed algorithm....
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...PRELIMINARIES A. DATASET In this work, thermal images from DMR-IR [8], [9] with a size of 640 × 480 pixels are employed to test the proposed approach....
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...In the second and third scenarios, the thermal breast images used in these experiments are taken from the DMR-IR dataset [9]....
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...Experiments show that the proposed method is robustness for the segmentation of different healthy and unhealthy cases images using the dataset from Mastology Research with Infrared Image (DMR-IR)....
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...In this work, thermal images from DMR-IR [8], [9] with a size of 640 × 480 pixels are employed to test the proposed approach....
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TL;DR: An IR thermographic sensor is applied for breast cancer detection and an automatic breast segmentation methodology, to spot the hottest regions in thermograms using the morphological watershed operator to help the experts locate the tumor.
Abstract: Breast cancer is the leading disease in incidence and mortality among women in developing countries. The opportune diagnosis of this disease strengthens the survival index. Mammography application is limited by age and periodicity. Temperature is a physical magnitude that can be measured by using multiple sensing techniques. IR (infrared) thermography using commercial cameras is gaining relevance in industrial and medical applications because it is a non-invasive and non-intrusive technology. Asymmetrical temperature in certain human body zones is associated with cancer. In this paper, an IR thermographic sensor is applied for breast cancer detection. This work includes an automatic breast segmentation methodology, to spot the hottest regions in thermograms using the morphological watershed operator to help the experts locate the tumor. A protocol for thermogram acquisition considering the required time to achieve a thermal stabilization is also proposed. Breast thermograms are evaluated as thermal matrices, instead of gray scale or false color images, increasing the certainty of the provided diagnosis. The proposed tool was validated using the Database for Mastology Research and tested in a voluntary group of 454 women of different ages and cancer stages with good results, leading to the possibility of being used as a supportive tool to detect breast cancer and angiogenesis cases.
42 citations
Cites background from "Asymmetry analysis of breast thermo..."
...The extraction of texture features and others, like energy, entropy or contrast, from the acquired thermograms, help to discriminate abnormalities, where aspects like high sensibility and specificity are always considered to provide better results to help specialists [42,43]....
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TL;DR: Thermography is a promising research problem and a potential solution for early detection of breast cancer in younger women, and supplementary research is needed to affirm the potential of this technology for predicting breast cancer risk effectively.
Abstract: Breast cancer is not preventable To reduce the death rate and improve the survival chances of breast cancer patients, early and accurate detection is the only panacea Delay in diagnosis of this disease causes 60% of deaths Thermal imaging is a low-risk modality for early breast cancer decision making without injecting any form of energy into the human body Thermography as a screening tool was first introduced and well accepted in 1956 However, a study in 1977 found that it lagged behind other screening tools and is subjective Soon after, its use was discontinued This review discusses various screening tools used to detect breast cancer with a focus on thermography along with their advantages and shortcomings With the maturation of thermography equipment and technological advances, this technique is emerging and has become the refocus of many biomedical researchers across the globe in the past decade This study dispenses an exhaustive review of the work done related to interpretation of breast thermal variations and confers the discipline, frameworks, and methodologies used by different authors to diagnose breast cancer Different performance metrics like accuracy, specificity, and sensitivity have also been examined This paper outlines the most pressing research gaps for future work to improvise the accuracy of results for diagnosis of breast abnormalities using image processing tools, mathematical modelling and artificial intelligence However, supplementary research is needed to affirm the potential of this technology for predicting breast cancer risk effectively Altogether, our findings inform that it is a promising research problem and a potential solution for early detection of breast cancer in younger women
33 citations
Cites methods from "Asymmetry analysis of breast thermo..."
...Block matching and 3D filtering techniques (BM3D) [30] were adopted for removing noise from breast thermograms and features extracted from denoised images were used to identify the abnormality....
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...In the studied literature, wavelet-based denoising [27, 28], block matching and 3D filtering technique (BM3D) [30] were the methods adopted for noise removal....
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...filtering techniques (BM3D) [30] were adopted for removing noise from breast thermograms and features extracted from denoised images were used to identify the abnormality....
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22 Feb 2018
TL;DR: The thermogram is more proper screening and has lower cost than other types of screening methods like the mammogram, ultrasound, and magnetic resonance imaging depending on a temperature of breast and surrounding area by using a special heat-sensing camera to determine the heat in the region of breasts.
Abstract: Cancer is considered as the leading cause of death among people The cancer is generated from uncontrolled growth for cells to collect them together to construct tumor One of these cancer types is breast cancer Detecting breast cancer, which is the second leading cause of death in women after lung cancer, depends on asymmetry in temperature between breasts If breast cancer can be detected at an early stage, it can save women life The thermogram is more proper screening and has lower cost than other types of screening methods like the mammogram, ultrasound, and magnetic resonance imaging depending on a temperature of breast and surrounding area by using a special heat-sensing camera to determine the heat in the region of breasts To classify healthy and unhealthy cases of breast cancer, methods are divided into image acquisition, preprocessing, segmentation, feature extraction and classification This paper focuses on reviewing the state-of-the-art methods and techniques of detecting and classifying the breast cancer using thermography images
20 citations
References
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TL;DR: An algorithm based on an enhanced sparse representation in transform domain based on a specially developed collaborative Wiener filtering achieves state-of-the-art denoising performance in terms of both peak signal-to-noise ratio and subjective visual quality.
Abstract: We propose a novel image denoising strategy based on an enhanced sparse representation in transform domain. The enhancement of the sparsity is achieved by grouping similar 2D image fragments (e.g., blocks) into 3D data arrays which we call "groups." Collaborative Altering is a special procedure developed to deal with these 3D groups. We realize it using the three successive steps: 3D transformation of a group, shrinkage of the transform spectrum, and inverse 3D transformation. The result is a 3D estimate that consists of the jointly filtered grouped image blocks. By attenuating the noise, the collaborative filtering reveals even the finest details shared by grouped blocks and, at the same time, it preserves the essential unique features of each individual block. The filtered blocks are then returned to their original positions. Because these blocks are overlapping, for each pixel, we obtain many different estimates which need to be combined. Aggregation is a particular averaging procedure which is exploited to take advantage of this redundancy. A significant improvement is obtained by a specially developed collaborative Wiener filtering. An algorithm based on this novel denoising strategy and its efficient implementation are presented in full detail; an extension to color-image denoising is also developed. The experimental results demonstrate that this computationally scalable algorithm achieves state-of-the-art denoising performance in terms of both peak signal-to-noise ratio and subjective visual quality.
7,912 citations
"Asymmetry analysis of breast thermo..." refers background in this paper
...The blocks that are similar are stacked together to form 3D array such that there else maximum similarity between blocks and minimum overlap between blocks [15]....
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01 Jan 1979
TL;DR: This survey reviews the image processing literature on the various approaches and models investigators have used for texture, including statistical approaches of autocorrelation function, optical transforms, digital transforms, textural edgeness, structural element, gray tone cooccurrence, run lengths, and autoregressive models.
Abstract: In this survey we review the image processing literature on the various approaches and models investigators have used for texture. These include statistical approaches of autocorrelation function, optical transforms, digital transforms, textural edgeness, structural element, gray tone cooccurrence, run lengths, and autoregressive models. We discuss and generalize some structural approaches to texture based on more complex primitives than gray tone. We conclude with some structural-statistical generalizations which apply the statistical techniques to the structural primitives.
5,112 citations
TL;DR: The proposed algorithm - very fast, automatic, robust and requiring low storage -provides an efficient smoother for numerous applications in the area of data analysis.
Abstract: A fully automated smoothing procedure for uniformly sampled datasets is described. The algorithm, based on a penalized least squares method, allows fast smoothing of data in one and higher dimensions by means of the discrete cosine transform. Automatic choice of the amount of smoothing is carried out by minimizing the generalized cross-validation score. An iteratively weighted robust version of the algorithm is proposed to deal with occurrences of missing and outlying values. Simplified Matlab codes with typical examples in one to three dimensions are provided. A complete user-friendly Matlab program is also supplied. The proposed algorithm, which is very fast, automatic, robust and requiring low storage, provides an efficient smoother for numerous applications in the area of data analysis.
936 citations
"Asymmetry analysis of breast thermo..." refers methods in this paper
...This denoising method is analyzed by evaluating signal to noise ratio (SNR) [17]....
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TL;DR: The performance and environmental requirements in characterizing thermography as being used for breast tumor screening under strict indoor controlled environmental conditions are discussed and potential errors and misinterpretations of the data derived from thermal imagers are considered.
Abstract: From the last 1.5 decades of complying with the strict standardized thermogram interpretation protocols by proper infrared trained personnel as documented in literature, breast thermography has achieved an average sensitivity and specificity of 90%. An abnormal thermogram is reported as the significant biological risk marker for the existence of or continues development of breast tumor. This review paper further discusses the performance and environmental requirements in characterizing thermography as being used for breast tumor screening under strict indoor controlled environmental conditions. The essential elements on performance requirements include display temperature color scale, display temperature resolution, emissivity setting, screening temperature range, workable target plane, response time and selection of critical parameters such as uniformity, minimum detectable temperature difference, detector pixels and drift between auto-adjustment. The paper however does not preclude users from potential errors and misinterpretations of the data derived from thermal imagers. © 2008 Elsevier Masson SAS. All rights reserved.
402 citations
"Asymmetry analysis of breast thermo..." refers background in this paper
...Thermography is considered as a reliable adjunct tool which has high sensitivity and specificity [2]....
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TL;DR: A new taxonomy based on image representations is introduced for a better understanding of state-of-the-art image denoising techniques and methods based on overcomplete representations using learned dictionaries perform better than others.
Abstract: Image denoising is a well explored topic in the field of image processing. In the past several decades, the progress made in image denoising has benefited from the improved modeling of natural images. In this paper, we introduce a new taxonomy based on image representations for a better understanding of state-of-the-art image denoising techniques. Within each category, several representative algorithms are selected for evaluation and comparison. The experimental results are discussed and analyzed to determine the overall advantages and disadvantages of each category. In general, the nonlocal methods within each category produce better denoising results than local ones. In addition, methods based on overcomplete representations using learned dictionaries perform better than others. The comprehensive study in this paper would serve as a good reference and stimulate new research ideas in image denoising.
376 citations
"Asymmetry analysis of breast thermo..." refers methods in this paper
...The 2D DCT transform is applied across the grouped blocks and 1D Harr transform is performed across the third dimension of a group along which blocks are stacked [6]....
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...This method perform nonlocal grouping there by achieves separation of noise by shrinkage and preserves unique features of images [6]....
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