Efficient mammographie mass segmentation techniques: A review
22 Mar 2017-pp 2253-2256
TL;DR: Various mass segmentation techniques used in mammogram images are compared to ensure the accuracy of the mammographic lesions predicted.
Abstract: Breast cancer is one of the common cause of mortality among women. For the early detection of breast cancer and other breast diseases a low dose x-ray imaging technique was introduced called mammography. However mammographic image analysis is extremely challenging. Computer Aided Diagnosis system is then considered as a second reader of the mammographic images in order to accurately predict the mammographic lesions. A Computer Aided Diagnosis system act as an intermediate between radiologist and an input image. New technologies are now emerging in the pitch of image processing especially in image segmentation. The segmentation is a process in which changing the representation of an image such that it is more easier to analyse. This paper compare various mass segmentation techniques used in mammogram images.
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TL;DR: This paper proposes ELM with Fruitfly Optimisation Algorithm (ELM-FOA) to tune the input weight to obtain optimum output at the ELM’s hidden node to obtain the solution analytically and can detect the calcifications and tumours with 99.04% accuracy.
Abstract: Breast cancer has been identified as one of the major diseases that have led to the death of women in recent decades. Mammograms are extensively used by physicians to diagnose breast cancer. The selection of appropriate image enhancement, segmentation, feature extraction, feature selection and prediction algorithm plays an essential role in precise cancer diagnosis on mammograms and remains as a major task in the research field. Classification methods predict the class label for unlabeled dataset based on its proximity to the learnt pattern. The selected features obtained after feature selection are classified using an extreme learning machines (ELM) to three classes with the classes being normal, benign and malignant. Low generalisation performance is the problem which happens due to the ill-conditioned output matrix of the hidden layer of the classifier. The optimisation algorithms would resolve these issues because of their global searching ability. This paper proposes ELM with Fruitfly Optimisation Algorithm (ELM-FOA) to tune the input weight to obtain optimum output at the ELM’s hidden node to obtain the solution analytically. The testing sensitivity and precision of ELM-FOA are 97.5% and 100% respectively. The developed method can detect the calcifications and tumours with 99.04% accuracy. The optimal selection of preprocessing and segmentation algorithms, features from multiple feature filters and the efficient classifier algorithm meliorate the performance of the approach.
30 citations
22 citations
TL;DR: This article suggests ELM with the Fruitfly Optimization Algorithm (ELM‐FOA) along with GSO to regulate the input weight to achieve maximal performance at the hidden node of the ELM.
Abstract: Breast imaging technique called mammography has gained bigger attention among the researchers for the diagnosis of breast malignancy in the woman. Mammogram screening is the most effective procedure to visualize various potential problems in the breast. The two most common features connected with breast tumors are mass lesions and microcalcification. The collection of suitable image preprocessing, segmentation, feature extraction, selection and prediction algorithms play an essential role in the accurate detection and classification of cancer on mammograms. Classification techniques estimate unlabeled datasets class labeling depending on its similarity to the pattern learned. The Glowworm Swarm Optimization(GSO) algorithm is ideal for finding several solutions, and dissimilar or equivalent objective function values at the same time. This feature of GSO is useful for optimizing the feature set obtained from multiscale feature extraction procedures. Poor performance in generalization is the issue that arises due to the unconditioned output matrix of the hidden stage of the ELM classifier. The optimization algorithms will address this matter because of their global search capabilities. This article suggests ELM with the Fruitfly Optimization Algorithm (ELM‐FOA) along with GSO to regulate the input weight to achieve maximal performance at the hidden node of the ELM. The testing precision and sensitivity of GSO‐ELM‐FOA are 100% and 97.91%, respectively. The system developed will detect the calcifications and tumors with an accuracy of 99.15%.
19 citations
Cites methods from "Efficient mammographie mass segment..."
...It improves image quality so that features available in them could be procured effectively.(16) Enhancement is the method of image pixel stretching to make the results more visible for further analysis of images....
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TL;DR: A novel computer-aided tool for automated sensing of normal tissue and abnormal masses from mammographic X-ray images is described, which demonstrated the proposed system as a competitive tool in assisting radiologists as it attains an average of 95.98 for accuracy, sensitivity, specificity, precision, recall, F1-score, and AUC.
Abstract: A novel computer-aided tool for automated sensing of normal tissue and abnormal masses from mammographic X-ray images is described. The pre-processing technique was firstly adopted for noise elimination on mammographic images. The automatic initialization of active contour was then placed on the pre-processed image for segmentation followed by deep convolutional neural networks to extract the features. Principal component analysis was then applied to choose the most significant features as input to the support vector machine classifier. Lastly, k-fold cross-validation techniques were executed for results validation. The developed tool was tested on public available datasets, namely Mammographic Image Analysis Society, and Digital Database for Screening Mammogram, based on eight evaluation methods: accuracy, sensitivity, specificity, receiver operating characteristic curve, area under curve (AUC), F1-score, precision, and recall. The outcome demonstrated the proposed system as a competitive tool in assisting radiologists as it attains an average of 95.24%, 93.94%, 96.61%, 94.66, 93.00%, 94.34%, and 0.98 for accuracy, sensitivity, specificity, precision, recall, F1-score, and AUC, respectively for testing on a combination of the aforementioned two datasets.
9 citations
Cites methods from "Efficient mammographie mass segment..."
...Using active contour and gradient vector flow (GVF) is considered as a PDE model since they are measured as a deformable model while combining two or more segmentation techniques are called hybrid [21], [22]....
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TL;DR: This article proposes ELM with the Grasshopper Optimization Algorithm (ELM‐GOA) to adjust the weight between the input and hidden layer to obtain maximum performance at the middle layer to ameliorating the accuracy and reducing the computational cost.
Abstract: Breast cancer considered to be a significant health issue among women. Early detection will ensure the treatment is easier and more successful. Recently, numerous methodologies have developed using medical imaging to investigate breast cancer. This research seeks to build a computer‐aided diagnostic (CAD) system to interpret mammograms. The first stage of CAD includes preprocessing, Fuzzy c means based segmentation applied to a localized area. In the second stage of the CAD method, the extraction of the feature is carried out using three distinct wavelet families with decomposition level at 4 and 6. The ANN, SVM, and ELM classifiers are used in the final stage to enable accurate classification. This article proposes ELM with the Grasshopper Optimization Algorithm (ELM‐GOA) to adjust the weight between the input and hidden layer to obtain maximum performance at the middle layer. This method adopts mammogram enhancement, optimum image segmentation, wavelet‐based feature extraction, and grasshopper optimization algorithm based ELM to ameliorating the accuracy and reducing the computational cost. The result shows that ELM‐GOA has precision and sensitivity of 100% and 98% respectively. The CAD system can identify tumors with 99.33 % accuracy.
8 citations
References
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TL;DR: The aim of this paper is to review existing approaches to the automatic detection and segmentation of masses in mammographic images, highlighting the key-points and main differences between the used strategies.
388 citations
01 Nov 2010
TL;DR: An automated technique for mammogram segmentation that uses morphological preprocessing and seeded region growing (SRG) algorithm in order to remove digitization noises, suppress radiopaque artifacts, and remove the pectoral muscle, for accentuating the breast profile region is explored.
Abstract: Mammography is currently the most effective imaging modality used by radiologists for the screening of breast cancer Finding an accurate, robust and efficient breast profile segmentation technique still remains a challenging problem in digital mammography Extraction of the breast profile region and the pectoral muscle is an essential pre-processing step in the process of computer-aided detection Primarily it allows the search for abnormalities to be limited to the region of the breast tissue without undue influence from the background of the mammogram The presence of pectoral muscle in mammograms biases detection procedures, which recommends removing the pectoral muscle during mammogram pre-processing In this paper we explore an automated technique for mammogram segmentation The proposed algorithm uses morphological preprocessing and seeded region growing (SRG) algorithm in order to: (1) remove digitization noises, (2) suppress radiopaque artifacts, (3) separate background region from the breast profile region, and (4) remove the pectoral muscle, for accentuating the breast profile region To demonstrate the capability of our proposed approach, digital mammograms from two separate sources are tested using Ground Truth (GT) images for evaluation of performance characteristics Experimental results obtained indicate that the breast regions extracted accurately correspond to the respective GT images
114 citations
TL;DR: Various filters used to improve image quality, remove the noise, preserves the edges within an image, enhance and smoothen the image are performed namely, average filter, adaptive median filter, average or mean filter, and wiener filter.
Abstract: Presently breast cancer detection is a very important role for worldwide women to save the life. Doctors and radio logistic can miss the abnormality due to inexperience in the field of cancer detection. The pre- processing is the most important step in the mammogram analysis due to poor captured mammogram image quality. Pre-processing is very important to correct and adjust the mammogram image for further study and processing. There are Different types of filtering techniques are available for pre- processing. This filters used to improve image quality, remove the noise, preserves the edges within an image, enhance and smoothen the image. In this paper, we have performed various filters namely, average filter, adaptive median filter, average or mean filter, and wiener filter.
85 citations
"Efficient mammographie mass segment..." refers methods in this paper
...In preprocessing, several filtering techniques are available, which are used to improve image quality, remove noise, preserving the edges within an image, enhance and smoothen the image [2]....
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01 Oct 2015
TL;DR: Combining several image enhancement algorithms to enhance the performance of breast-region segmentation and to segment breast region in order to obtain better visual interpretation, analysis, and classification of mammogram masses to assist radiologists in making more accurate decisions is investigated.
Abstract: Reveal the optimal combination of various enhancement methods.Segment breast region in order to obtain better visual interpretation.To assist radiologists in making accurate decisions, analysis and classifications.Tumor classification accuracy and sensitivity values of 81.1% and 86%, respectively.Participated radiologists are pleased with the results and acknowledged the work. Mammography is the most effective technique for breast cancer screening and detection of abnormalities. However, early detection of breast cancer is dependent on both the radiologist's ability to read mammograms and the quality of mammogram images. In this paper, the researchers have investigated combining several image enhancement algorithms to enhance the performance of breast-region segmentation. The masses that appear in mammogram images are further analyzed and classified into four categories that include: benign, probable benign and possible malignant, probable malignant and possible benign, and malignant. The main contribution of this work is to reveal the optimal combination of various enhancement methods and to segment breast region in order to obtain better visual interpretation, analysis, and classification of mammogram masses to assist radiologists in making more accurate decisions. The experimental dataset consists of a total of more than 1300 mammogram images from both the King Hussein Cancer Center and Jordan Hospital. Results achieved tumor classification accuracy values of 90.7%. Moreover, the results showed a sensitivity of 96.2% and a specificity of 94.4% for the mass classifying algorithm. Radiologists from both institutes have acknowledged the results and confirmed that this work has lead to better visual quality images and that the segmentation and classification of tumors has aided the radiologists in making their diagnoses.
78 citations
"Efficient mammographie mass segment..." refers methods in this paper
...Several enhancement algorithms are combined by Nijad AlNajdawi [12] so as to enhance the breast region segmentation performance....
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TL;DR: This study shows the outcome of applying image processing threshold, edge based and watershed segmentation on mammogram breast cancer image and also presents a case study between them based on time consuming and simplicity.
Abstract: is a special case of CT scan who adopts X-ray method & uses the high resolution film so that it can detect well the tumors in the breast. Low radiation is the strength of this method. Mammography is especially used only in the breast tumor detection Mammogram breast cancer images have the ability to assist physicians in detecting disease caused by cells normal growth. Developing algorithms and software to analyse these images may also assist physicians in there daily work. This study that shows the outcome of applying image processing threshold, edge based and watershed segmentation on mammogram breast cancer image and also presents a case study between them based on time consuming and simplicity. The real-time implementation of this paper can be implemented using data acquisition hardware and software interface with the mammography systems. KEYWORDSprocessing; CT scan; Low Radiation; Watershed Image Segmentation; Data acquisition; Mammography; X-ray
51 citations
"Efficient mammographie mass segment..." refers methods in this paper
...[18] proposed an approach to identify breast cancer mass and calcification using K means and Fuzzy C means clustering algorithm....
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