A ROBUST APPROACH TO CLASSIFY MICROCALCIFICATION IN DIGITAL MAMMOGRAMS USING CONTOURLET TRANSFORM AND SUPPORT VECTOR MACHINE

In this paper, we present a novel approach to detect microcalcifications (MCCs) in the mammograms. Microcalcification has approximately a size of several pixels in diameter on early cancer mammograms and has low contrast. So it is difficult to detection the microcalcifications. The essential idea of propose approach is to apply morphological and wavelet transform to locate the micro calcifications in digital mammograms. The presented method overcome higher true positive rate (TPR) but higher false positive (FP) problem by using single wavelet transform or morphology. The experiment results show the presented method has higher TPR and reducing FP obviously. Using the mammogram data form the Mammographic Image Analysis Society (MIAS) database the TPR is 80.2% at the cost of average 2.5 false microcalcifications per region of interest (ROI)

Mammography is one of the most efficient imaging techniques for the early detection and diagnosis of breast cancer in woman. The appearance of microcalcifications (MCs) in X-ray mammograms can be regarded as important early diagnostic feature of breast cancer. However, the detection of MCs in X-ray mammograms only depend on radiologists by visual inspection is a difficult task. In this paper, a novel algorithm for the automated detection of MCs in digital mammograms is described. The aim of approach is to locate suspicious MCs by analyzing the distribution of brightness over the digital mammograms. The algorithm for the detection of MCs is performed in two steps. First, the digital X-ray mammogram intensity surface is well-fitted by the least squares support vector machines (LS-SVM), and then the maximum extremum points are detected on the fitted intensity surface by convolving the image with the second order directional derivative operators deduced from the mapped LS-SVM with mixture of kernels. The proposed approach is developed and evaluated by using MIAS database. The experimental results demonstrate the possible MCs are detected accurately and efficiently.

This paper presents an approach for early breast cancer diagnostic by employing combination of artificial neural networks (ANN) and wavelet based subband image decomposition which detect microcalcification in digital mammograms. The microcalcifications correspond to high-frequency components of the image spectrum, detection of microcalcifications is achieved by decomposing the mammograms into different frequency subbands, suppressing the low-frequency subband, and finally, reconstructing the mammogram from the subbands containing only high frequencies. For this approach we employed different types of wavelet packets. We used the result as an input of neural network for classification. The proposed methodology is tested using the Nijmegen and the Mammographic Image Analysis Society (MIAS) mammographic databases and images collected from local hospitals. Results are presented as the receiver operating characteristic (ROC) performance and are quantified by the area under the ROC curve.

Detection of suspicious masses in mammograms play a vital role in early diagnosis of breast cancer, to reduce the death rate among women. The presence of masses and calcification’s in mammograms is distinguishing signs for breast cancer diagnosis. But in some cases, due to contrast variation, fuzzy boundaries and presence of noise in mammograms, segmentation and detection of masses is challenging assignment. This paper presents a new segmentation approach to detect masses in mammographic images. The proposed approach consist of artifacts elimination and pectoral region extraction, suspicious mass enhancement using dual morphological operation technique and finally, extraction of Regions of Interest (ROIs) from background using scaled Reyni entropy. The proposed system has been tested on two data-sets i.e. mini- Mammographic Image Analysis Society (mini-MIAS) and Digital Database for Screening Mammography (DDSM), over 50 and 90 mammograms respectively. Performance achieved with proposed system in terms of True Positive Fraction (TPF) yields 93.2% and 93.9% respectively, at the rate of 1.48 and 0.74 average False Positive per Image (FP/I), tested on both Cranio-Caudal (CC) and Medio-Lateral Oblique (MLO) views. The obtained experimental results demonstrates that proposed method gives improved results for mass detection and can be useful for radiologists in diagnosis of breast cancer at early stage.

This paper presents a machine learning based approach for the discrimination of malignant and benign microcalcification (MC) clusters in digital mammograms. A series of morphological operations was carried out to facilitate the feature extraction from segmented microcalcification. A combination of morphological, texture, and distribution features from individual MC components and MC clusters were extracted and a correlation-based feature selection technique was used. The clinical relevance of the selected features is discussed. The proposed method was evaluated using three different databases: Optimam Mammography Image Database (OMI-DB), Digital Database for Screening Mammography (DDSM), and Mammographic Image Analysis Society (MIAS) database. The best classification accuracy (%) was achieved for OPTIMAM using a stack generalization classifier with 10-fold cross validation obtaining an A value equal to .

This paper presents a novel infrared (IR) and visible images fusion methodology based on non-subsampled contourlet transform (NSCT). NSCT shows better performance compared with usual multi-scale decomposition for its multi-scale, shift invariance, multi-direction and efficient capture of geometric structures. The proposed fusion method uses NSCT for multiresolution decomposition of the source images. The low-pass NSCT adaptive fusion weights calculated from the IR source image’s pixel statistical characteristics. The high frequency directional coefficients with max absolute value are the coefficients of the fusion NSCT high frequency. Experimental results conforms that the proposed method have better performance compared with DWT, compressed sensing based on DWT (CS-DWT), NSCT, NSCT based on spatial frequency motivated pulse coupled neural networks (SF-PCNN-NSCT) from visual effects and a list of fusion quality evaluation metrics.

This chapter explores diagnosis of the breast tissues as normal, benign, or malignant in digital mammography, using computer-aided diagnosis (CAD). System for the early diagnosis of breast cancer can be used to assist radiologists in mammographic mass detection and classification. This chapter presents an evaluation about performance of extracted features, using gray-level co-occurrence matrix applied to all detailed coefficients. The nonsubsampled contourlet transform (NSCT) of the region of interest (ROI) of a mammogram were used to be decomposed in several levels. Detecting masses is more difficult than detecting microcalcifications due to the similarity between masses and background tissue such as F) fatty, G) fatty-glandular, and D) dense-glandular. To evaluate the system of classification in which k-nearest neighbors (KNN) and support vector machine (SVM) used the accuracy for classifying the mammograms of MIAS database between normal and abnormal. The accuracy measures through the classifier were 94.12% and 88.89% sequentially by SVM and KNN with NSCT.

An automatic microcalcification detection system based on a hybrid neural network classifier

The 3D engineering surfaces are comprised of a range of spatial frequency components, such as form, waviness and roughness. Filtering techniques are commonly adopted to separate the different components. To overcoming the shortcomings of traditional filtering method, a new separation method is proposed with the correlation analysis of sub-bands in nonsubsampled contourlet transform (NSCT) domain. The 3D engineering surface topography is decomposed into different sub-bands by NSCT, and the correlation coefficients of NSCT sub-bands with its parent and children sub-bands are calculated by Pearson correlation method. Then the roughness, waviness and form of 3D real surface topography are restructured respectively by the inverse NSCT based on the NSCT sub-bands which belong to different components. Finally, a group of 3D engineering surfaces are separated into different components, and the result shown that the proposed method can separate 3D engineering surface effectively.

World’s second most occurring cancer is breast cancer. Prediction of disease is one of the most challenging tasks and there are many factors that effect this type of diagnosis like the ability of visual perception. This paper proposed a Convolutional Neural Network (CNN) based proper method for analyzing the earliest signs of breast cancer with the help of mammogram images. The main goal of proposed system is to identify the disease of breast cancer at early stages. Due to this reason, Mammographic image analysis society (MIAS) dataset is used. There are three hundred & twenty two (322) mammograms in the dataset, with 209 images of normal breasts and 133 images of abnormal breasts. While abnormal breasts are further classified as benign (62 images) and malignant (51 images). To implement this system, python library Keras and Tensor Flow libraries are used along with deep learning model CNN. Convolutional Neural Network (CNN) has been shown to be effective in detecting breast cancer in mammography images with 70% accuracy rate, according to promising testing data. The proposed system will enable the radiologist in detecting breast cancer in early stages.

The Breast Cancer is second of the dangerous type diseases that is very effective for women in the world. Breast Cancer is spread very fast in last stage. It’s not give a more time for patients for treatment. We need accurate and efficient diagnosis system over a short period of time that can processed a large amount of data in a short time. It’s only possible using deep learning techniques. We are studying on a new diagnosis system for detecting Breast cancer in early stage. But in this paper we are describing the all techniques and images processing method for segmentation and filter images for breast cancer diagnosis. In this paper we have describe all algorithm and techniques for image pre-processing and post progressing. We have reviewed more than 20 papers from different publisher and find out that many researcher working on breast cancer diagnosis techniques. It’s a very latest and effective techniques for helping and saving the human life. In this paper we have also describe some experimental part of images segmentation and filter images with different filter method. It’s a very interesting area for researcher. In this paper we have used (Mammography Image Analysis Society) MIAS dataset having 95 images in .pgm format with 1024 X 1024 pixel. All paper divided in two main part first is data collection and image pre-processing. In this paper we have describe some most used deep learning pre-trained model that is very effective for images pre-processing. As per reviewed study the AlexNet, GoogleNet and VGG16 are provided best accuracy for detecting breast cancer in mammography images. We also used many filter method for image quality improvement. Filters are used to filter unwanted things or object in a spatial domain or surface. The main objective of the filter are to improve the quality of the images by enhancing and improve interoperability of the images. In this paper we also showing the comparison between most used pre-trained model that are used for breast cancer diagnosis.

The presence of microcalcification clusters in digital mammograms is a primary indicator of early stages of malignant types of breast cancer and its detection is important to prevent the disease. This paper uses a procedure for the classification of microcalcification clusters in mammograms using sequential Difference of Gaussian filters (DoG) and feedforward Neural Networks (NN). Three methods using class separability, forward sequential search and genetic algorithms for feature selection are compared. We found that the use of Genetic Algorithms (GAs) for selecting the features from microcalcifications and microcalcification clusters that will be the inputs of a feedforward Neural Network (NN) results mainly in improvements in overall accuracy, sensitivity and specificity of the classification.

Computer aided detection and classification system has been developed to detect breast cancer at an early stage by predicting the area and texture of malignant tumours. Noise removal and image enhancement is carried out in the pre-processing stage by using adaptive median filter and contrast limited histogram equalisation techniques. Improved watershed segmentation technique with appropriate internal and external markers, have proved to be an efficient approach in detecting the region of interest. The detected tumours are classified using feedforward artificial neural network that are trained using textural features. Area and entropy features extracted from malignant tumours aids in early detection of breast cancer by categorising malignant tumours as belonging to stage I or stage II. The overall efficiency of the system, for identifying stages of malignant tumour is 92%, which has been identified to be high when compared to all existing systems. Mammogram images from Mammographic Image Analysis Society (MIAS) database was used for training the system and efficiency of the system was tested using real time hospital images.

Computer aided detection system for micro calcifications in digital mammograms

Script identification is an important step in multi-script recognition. Despite the achieved results in this field, the identification of Central Asian scripts has not been considered in-depth. In the Central Asian region, there are many similar scripts, and the traditional texture features can not discriminate them accurately. This paper proposes a script identification method based on fused texture features for Central Asian document images. On preprocessed multilingual document images, the method first performs Non-subsampled Contourlet Transform (NSCT), and then extracts Tamura texture features of the generated sub-bands. A Support Vector Machine (SVM) classifier is trained for classification. For experimental evaluation, it is collected a dataset of 30, 000 document images for 10 scripts, such as Arabic, Chinese, English, Russian, Kazakhstan, Turkish, Uyghur, Kyrgyzstan, Mongolian and Tibetan. The experimental results show that the proposed method can extract multi-scale and multi-directional texture features, and the fusion of texture features leads to superior performance of script identification.