International Journal of Computer and Applications

The increasing use of biomarkers in cancer have led to the concept of personalized medicine for patients. Personalized medicine provides better diagnosis and treatment options available to clinicians. Radiological imaging techniques provide an opportunity to deliver unique data on different types of tissue. However, obtaining useful information from all radiological data is challenging in the era of ‘big data’. Recent advances in computational power and the use of genomics have generated a new area of research termed Radiomics. Radiomics is defined as the high throughput extraction of quantitative imaging features or texture (radiomics) from imaging to decode tissue pathology and creating a high dimensional data set for feature extraction. Radiomic features provide information about the gray-scale patterns, inter-pixel relationships. In addition, shape and spectral properties can be extracted within the same regions of interest on radiological images. Moreover, these features can be further used t...

Radiomics: a new application from established techniques

The ultrasound imaging is one of the most common schemes to detect diseases in the clinical practice. There are many advantages of ultrasound imaging such as safety, convenience, and low cost. However, reading ultrasound imaging is not easy. To support the diagnosis of clinicians and reduce the load of doctors, many ultrasound computer-aided diagnosis (CAD) systems are proposed. In recent years, the success of deep learning in the image classification and segmentation led to more and more scholars realizing the potential of performance improvement brought by utilizing the deep learning in the ultrasound CAD system. This paper summarized the research which focuses on the ultrasound CAD system utilizing machine learning technology in recent years. This study divided the ultrasound CAD system into two categories. One is the traditional ultrasound CAD system which employed the manmade feature and the other is the deep learning ultrasound CAD system. The major feature and the classifier employed by the traditional ultrasound CAD system are introduced. As for the deep learning ultrasound CAD, newest applications are summarized. This paper will be useful for researchers who focus on the ultrasound CAD system.

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Machine Learning in Ultrasound Computer-Aided Diagnostic Systems: A Survey.

Ultrasound (US) imaging is the most commonly performed cross-sectional diagnostic imaging modality in the practice of medicine. It is low-cost, non-ionizing, portable, and capable of real-time image acquisition and display. US is a rapidly evolving technology with significant challenges and opportunities. Challenges include high inter- and intra-operator variability and limited image quality control. Tremendous opportunities have arisen in the last decade as a result of exponential growth in available computational power coupled with progressive miniaturization of US devices. As US devices become smaller, enhanced computational capability can contribute significantly to decreasing variability through advanced image processing. In this paper, we review leading machine learning (ML) approaches and research directions in US, with an emphasis on recent ML advances. We also present our outlook on future opportunities for ML techniques to further improve clinical workflow and US-based disease diagnosis and characterization.

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Machine learning for medical ultrasound: status, methods, and future opportunities.

Symtosis: A liver ultrasound tissue characterization and risk stratification in optimized deep learning paradigm.

Feature extraction and feature normalization is an important preprocessing technique, usually employed before classification. Feature normalization is a useful step to restrict the values of all features within predetermined ranges. However, appropriate choice of normalization technique and normalization range is an important issue, since, applying normalization on the input could change the structure of data and thereby affecting the outcome of multivariate analysis and calibration used in data mining and pattern recognition problems. This paper investigates and evaluates some popular feature normalization techniques and studies their impact on performance of classifier with application to breast tumor classification using ultrasound images. For evaluating the feature normalization techniques, back-propagation artificial neural network [BPANN] and support vector machine [SVM] classifier models are used. Results show that that normalization of features has significant effect on the classification accuracy. General Terms Pattern Recognition, Medical Image Processing.

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Investigations on Impact of Feature Normalization Techniques on Classifier's Performance in Breast Tumor Classification

New classification approach is developed.Proposed approach eliminates ambiguous and doubtful samples from training dataset.Proposed approach is used to classify breast tumors in ultrasound images.Proposed classifier outperforms conventional ones.Proposed method achieves high classification accuracy. The performance of supervised classification algorithms is highly dependent on the quality of training data. Ambiguous training patterns may misguide the classifier leading to poor classification performance. Further, the manual exploration of class labels is an expensive and time consuming process. An automatic method is needed to identify noisy samples in the training data to improve the decision making process. This article presents a new classification technique by combining an unsupervised learning technique (i.e. fuzzy c-means clustering (FCM)) and supervised learning technique (i.e. back-propagation artificial neural network (BPANN)) to categorize benign and malignant tumors in breast ultrasound images. Unsupervised learning is employed to identify ambiguous examples in the training data. Experiments were conducted on 178 B-mode breast ultrasound images containing 88 benign and 90 malignant cases on MATLABź software platform. A total of 457 features were extracted from ultrasound images followed by feature selection to determine the most significant features. Accuracy, sensitivity, specificity, area under the receiver operating characteristic curve (AUC) and Mathew's correlation coefficient (MCC) were used to access the performance of different classifiers. The result shows that the proposed approach achieves classification accuracy of 95.862% when all the 457 features were used for classification. However, the accuracy is reduced to 94.138% when only 19 most relevant features selected by multi-criterion feature selection approach were used for classification. The results were discussed in light of some recently reported studies. The empirical results suggest that eliminating doubtful training examples can improve the decision making performance of expert systems. The proposed approach show promising results and need further evaluation in other applications of expert and intelligent systems.

Fuzzy cluster based neural network classifier for classifying breast tumors in ultrasound images

An Enhancement in Adaptive Median Filter for Edge Preservation

Adaptive Gradient Descent Backpropagation for Classification of Breast Tumors in Ultrasound Imaging

With advancements in machine learning algorithms and computer aided diagnostic (CAD) systems, the performance of automated analysis of radiological images has improved substantially in recent times. However, the lack of integration between the radiologist and CAD systems restrains the rate of progress as well as the reach of such advancements in clinical use. This article aims to improve the clinical efficiency of ultrasound based CAD systems for classification of breast lesions by integrating back-propagation artificial neural network (BPANN), support vector machine (SVM) and radiologist feedback. The acquired breast ultrasound images were subjected to wavelet based filtering in order to reduce speckle noise followed by feature extraction, feature selection and classification. Experiments on a database of 178 ultrasound images of breast anomalies (88 benign and 90 malignant) show that the proposed methodology achieves classification accuracy of 98.621% and 98.276%, respectively, when all 457 and 19 most relevant features selected by multi-criteria feature selection method were used for classification. The accuracy achieved is significantly higher than that using conventional classifiers based on BPANN and SVM. Further, it is found that integrating expert opinion in CAD systems improves its overall performance. The quantitative results obtained are discussed in light of some recently reported studies.

Integrating radiologist feedback with computer aided diagnostic systems for breast cancer risk prediction in ultrasonic images: An experimental investigation in machine learning paradigm

A. S. Thoke

Papers

Investigations on Impact of Feature Normalization Techniques on Classifier's Performance in Breast Tumor Classification

Fuzzy cluster based neural network classifier for classifying breast tumors in ultrasound images

An Enhancement in Adaptive Median Filter for Edge Preservation

Adaptive Gradient Descent Backpropagation for Classification of Breast Tumors in Ultrasound Imaging

Integrating radiologist feedback with computer aided diagnostic systems for breast cancer risk prediction in ultrasonic images: An experimental investigation in machine learning paradigm