Computer aided diagnosis for interval change analysis of lung nodule Clinical experience with a computer aided diagnosis system for automatic Performance levels for computerized detection of nodules in different size Subscribe to Digital Library. Receive Erratum Email Alert. Erratum Email Alerts notify you when an article has been updated or the paper is withdrawn.
Visit My Account to manage your email alerts. Email or Username Forgot your username? Password Forgot your password? Keep me signed in. No SPIE account? Create an account Institutional Access:. The alert successfully saved. The alert did not successfully save. Please try again later. Frangi, "Computer-aided detection of lung nodules: a review," J. Citation Only. RIS Zotero. Ref Works. Morphological opening helps to smoothen the edges and breaks the weak connections.
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Also it helps to remove the unwanted regions that do not contain structuring elements. The morphological closing operation is denoted by Eq. It is union of all translations of B without overlap on A.
Computer-Aided Diagnosis Systems for Lung Cancer: Challenges and Methodologies
It helps to join the weak edges and fill the breaks in the edges. Also it helps to fill gaps and small holes in the structuring elements. After calculation of gradient, the proposed method finds the regional minima, on which watershed transformation is applied. Then the mask is imposed on the gradient image. But it results in oversegmentation. Then the morphological operations, such as opening and closing, are applied to minimize the regions and fill the gap between the edges. Experimental results of threshold-based segmentation a original image from the mini-MIAS database, b preprocessed image, c opening, d closing, e reconstructed from opening-closing, and f mass identified fourth level of thresholding.
The traditional watershed method has the disadvantage of oversegmentation; hence, marker controlled watershed segmentation is used to extract the mass part from breast profile. The preprocessed image passed to the gradient of the image with sobel operator to smoothen the edges. Then traditional watershed is applied to oversegments. Applying morphological operation opening followed by closing helped to find regional maximum and minimum values to apply watershed segmentation.
This method does not work well on dense and low contrast images, either it over segments or it miss the mass part in segmentation. Step-wise results of modified watershed segmentation method. The contour-based segmentation algorithm works in five steps as follows: Step 1: Read the preprocessed image as input image. Step 2: By performing the morphological operations, the abnormality is super imposed on original image. Step 3: Apply active contour technique to identify the suspicious lesions; the suspicious lesions are peaks of the contour.
Energy of the contour is calculated by adding the intensity of pixels from each contour and finding average. Average of each contour is compared to select the mass region.
Experimental results of contour-based segmentation technique a original image, b preprocessed image, c opening, d closing, e reconstructed from opening and closing, f active contour. Also it works with high-intensity and low-intensity images. Mass segmented on different tissues using contour-based segmentation. Radiologists depict masses by their shape, gray levels, and texture properties.
The properties of mass surroundings are important discriminators from the background tissue. The shape of the mass changing from early benign to malignant as round, oval, lobular, or irregular circumscribed, micro-lobulated, obscured, indistinct, or peculated [ 36 — 39 ]. We also note that masses with speculated and indistinct boundaries have a greater probability of malignancy than circumscribed masses. It also notes that masses with speculated and indistinct boundaries have a greater probability of malignancy than circumscribed masses. Along with the mass margin and shape, intensity of gray level is one of major feature to classify the mass.
The DWT is wavelet transform using discrete set of scales and translations followed by some rules. To use a wavelet, it is necessary to discretize with respective to scale parameters, i. Thus, the family of wavelet is defined in Eq. For a discrete time signal x [ n ], the decomposition is given by Eq.
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In case of images, the DWT is applied to each dimensionality, separately. The resulting image X is decomposed in first level is xA, xH,xV, and xD as approximation, horizontal, vertical, and diagonal, respectively. The xA component contains low frequency components and remaining contains high frequency component. Then, DWT applied to xA for second level decomposition.
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Hence, the wavelet provides hierarchical framework to interpret the image information [ 40 , 41 ]. The basis of wavelet transform is localized on mother wavelet. Hence, in the proposed work, Haar, Daubechies db2,db4 and db8 , coiflet and bi-orthogonal wavelets at decomposition of level 4 used for the dataset and passed feature vector for the classification.
Shape Analysis in Medical Image Analysis | tyruvyvizo.cf
In texture analysis, widely used features are GLCM features. The GLCM is representation of frequently occurred gray levels combinations [ 42 ]. Depends on the number of combinations the statistics are measured as features in first order, second order, and in higher orders. Initially Haralick et al. In most of the CAD systems, these gray level features are used to interpret the symptoms.
In the proposed work, we have extracted 21 GLCM features which are contributing to the discrimination of mass type. Example of GLCM a four-level gray image, b direction of combination with single pixel distance, c covariance matrix of four levels with direction 00 with single pixel distance, and d co-variance matrix of four levels with direction with single pixel distance.
Texture feature extraction is time-consuming process with basic filters because of scale and time invariant. SFTA works on multilevel thresholding on gray image. In purpose of using SFTA is to get the clear structure for mass boundaries. The 21 texture feature vector corresponds to texture information like dimension, different gray levels, and area of ROI.
All together there are 73 features extracted from mass to train the CAD system to discriminate the mass type as benign and malignant [ 48 ]. Support vector machine SVM is a supervised learning technique that seeks an optimal hyperplane to separate two classes of samples. Mapping the input data into a higher dimensional space is done by using Kernel functions with the aim of obtaining a better distribution of the data.
Then, an optimal separating hyperplane in the high-dimensional feature space can be easily found as shown in Ref. MIAS contains a total of mammograms of both breasts left and right of patients. According to above definitions of true positive, true negative, false positive and false negative. The equations related to specificity the accuracy of negative class , sensitivity accuracy of positive class and accuracy , and accuracy of recognize both negative and positive classes are defined as in Eqs.
The accuracy is high using wavelet db4 features [ 50 ].
Though wavelet db4 gives high accuracy, it is important to consider texture based and gray level features to discriminate the mass type as benign and malignant. Comparative analysis of accuracy rate for adaptive threshold, modified watershed, and energy-based contour segmentation techniques. Early detection of breast cancer may reduce the death rate.
The advancement in technology is needed in the detection of all types of masses in terms of increasing sensitivity and reducing false positive rate. Masses can be varying in size and shape and thus, the proposed segmentation and feature extraction techniques are more suitable to measure in terms. It proved that with less number of features and simple classifier, it improved the accuracy of detection and classification with less complexity.
It used as a second opinion by the radiologists. Improving CAD accuracy increases the treatment option and a cure is more likely. There are some commercial CAD systems that have been reported, which are R2 technology Inc, intelligent system software Inc. All of these commercial CAD systems perform better at detecting calcifications than the masses. Architectural distortions become the challenging task to all the commercial CAD system.
Computer Aided Diagnosis - Medical Image Analysis Techniques
One cannot make a direct comparison between these systems and their work because there is no same clinical dataset to study and compare the performances. The proposed CAD model is more suitable for mass detection and classification. The obtained result show that selection of suitable approaches to design an algorithm for CAD is subject to the accuracy, sensitivity, and false positive identifications.
To remove background noise and pectoral muscle, region growing and thresholding methods are proved to be good. Mass in mammography is extracted with proper marking use of contour-based segmentation. The set relevant features are provided to SVM classifier to discriminate mass type as benign or malignant. Finally, the outcomes from this study predict that the selection of appropriate technique at each stage of medical image analysis is subjective to relevant and significant to design a CAD model. Licensee IntechOpen.
This chapter is distributed under the terms of the Creative Commons Attribution 3. Help us write another book on this subject and reach those readers. Login to your personal dashboard for more detailed statistics on your publications. Edited by Cherie M. Edited by Shaul Mordechai. We are IntechOpen, the world's leading publisher of Open Access books. Built by scientists, for scientists. Our readership spans scientists, professors, researchers, librarians, and students, as well as business professionals. Downloaded: Abstract Breast cancer is the second leading cause of death among women worldwide.
Keywords breast cancer computer-aided diagnosis segmentation feature extraction classification. Introduction In medical imaging field, computer-aided detection CADe or computer-aided diagnosis CADx is the computer-based system that helps doctors to take decisions swiftly [ 1 , 2 ]. Objectives of the CAD system The main goal of CAD systems is to identify abnormal signs at an earliest that a human professional fails to find.
CAD for breast cancer Breast cancer ranks as second leading cause of death in women worldwide. Segmentation Disparity of different structures in the image, e. Evaluation of CAD systems Evaluation of CAD systems measured by two major factors, such as sensitivity and specificity, they seek for suspicious structure. Materials and methods 2.
Dataset used There are many standard datasets that are recommended by researchers to test CAD algorithms for mammography. Methodology Breast cancer diagnosis requires systematic image analysis and characterization and integration of numerous clinical and mammographic variables, which are difficult and error-prone tasks for physicians [ 14 , 15 ]. Preprocessing Preprocessing is the foremost task in medical imaging, it helps to identify the abnormal part, which cannot be recognized by visualizing the image but can be detected through CAD systems.
Removal of background Histogram is the traditional method to remove the background. Removal of pectoral muscle Another challenging task in preprocessing of mammography is removal of pectoral muscle [ 21 ]. Segmentation of mass Segmentation is the process of partitioning the abnormal part from the normal part. Adaptive thresholding segmentation techniques Thresholding is yet effective and simple method of segmenting the image into different regions.
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Mathematical morphological operations Mathematical morphological operations help to structure elements and measure the shape of the image. Modified watershed segmentation The traditional watershed method has the disadvantage of oversegmentation; hence, marker controlled watershed segmentation is used to extract the mass part from breast profile.
Contour-based segmentation technique The contour-based segmentation algorithm works in five steps as follows: Step 1: Read the preprocessed image as input image. Step 4: Extract peak of the contour by calculating the energy of each contour. Step 5: Mark extracted contour as ROI. Feature extraction of mass ROI Radiologists depict masses by their shape, gray levels, and texture properties. SFTA features Texture feature extraction is time-consuming process with basic filters because of scale and time invariant.
Classification Support vector machine SVM is a supervised learning technique that seeks an optimal hyperplane to separate two classes of samples. Confusion matrix. Samples used for performance evaluation. Segmentation techniques Accuracy Specificity Sensitivity Adaptive threshold based The performance measures of the SVM classifier with different similarity matrices.
Tzikopoulos et al. Liasis et al.