Jinshan Tang

Bio: Jinshan Tang is an academic researcher from Michigan Technological University. The author has contributed to research in topics: Image segmentation & Segmentation. The author has an hindex of 25, co-authored 120 publications receiving 2976 citations. Previous affiliations of Jinshan Tang include George Mason University & Alcorn State University.

Computer-Aided Detection and Diagnosis of Breast Cancer With Mammography: Recent Advances

Abstract: Breast cancer is the second-most common and leading cause of cancer death among women. It has become a major health issue in the world over the past 50 years, and its incidence has increased in recent years. Early detection is an effective way to diagnose and manage breast cancer. Computer-aided detection or diagnosis (CAD) systems can play a key role in the early detection of breast cancer and can reduce the death rate among women with breast cancer. The purpose of this paper is to provide an overview of recent advances in the development of CAD systems and related techniques. We begin with a brief introduction to some basic concepts related to breast cancer detection and diagnosis. We then focus on key CAD techniques developed recently for breast cancer, including detection of calcifications, detection of masses, detection of architectural distortion, detection of bilateral asymmetry, image enhancement, and image retrieval.

Image enhancement using a contrast measure in the compressed domain

Abstract: An image enhancement algorithm for images compressed using the JPEG standard is presented. The algorithm is based on a contrast measure defined within the discrete cosine transform (DCT) domain. The advantages of the psychophysically motivated algorithm are 1) the algorithm does not affect the compressibility of the original image because it enhances the images in the decompression stage and 2) the approach is characterized by low computational complexity. The proposed algorithm is applicable to any DCT-based image compression standard, such as JPEG, MPEG 2, and H. 261.

Mass Classification in Mammograms Using Selected Geometry and Texture Features, and a New SVM-Based Feature Selection Method

Abstract: Masses are the primary indications of breast cancer in mammograms, and it is important to classify them as benign or malignant Benign and malignant masses differ in geometry and texture characteristics However, not every geometry and texture feature that is extracted contributes to the improvement of classification accuracy; thus, to select the best features from a set is important In this paper, we examine the feature selection methods for mass classification We integrate a support vector machine (SVM)-based recursive feature elimination (SVM-RFE) procedure with a normalized mutual information feature selection (NMIFS) to avoid their singular disadvantages (the redundancy in the selected features of the SVM-RFE and the unoptimized classifier for the NMIFS) while retaining their advantages, and we propose a new feature selection method, which is called the SVM-RFE with an NMIFS filter (SRN) In addition to feature selection, we also study the initialization of mass segmentation Different initialization methods are investigated, and we propose a fuzzy c-means (FCM) clustering, with spatial constraints as the initialization step In the experiments, 826 regions of interest (ROIs) from the Digital Database for Screening Mammography were used All 826 were used in the classification experiments, and 413 ROIs were used in the feature selection experiments Different feature selection methods, including F-score, Relief, SVM-RFE, SVM-RFE with a minimum redundancy-maximum relevance (mRMR) filter [SVM-RFE (mRMR)], and SRN, were used to select features and to compare mass classification results using the selected features In the classification experiments, the linear discriminant analysis and the SVM classifiers were investigated The accuracy that is obtained with the SVM classifier using the selected features obtained by the F-score, Relief, SVM-RFE, SVM-RFE (mRMR), and SRN methods are 88%, 88%, 90%, 91%, and 93%, respectively, with a tenfold cross-validation procedure, and 91%, 89%, 92%, 92%, and 94%, respectively, with a leave-one-out (LOO) scheme We also compared the performance of the different feature selection methods using the receiver operating characteristic analysis and the areas under the curve (AUCs) The AUCs for the F-score, Relief, SVM-RFE, SVM-RFE (mRMR), and SRN methods are 09014, 08916, 09121, 09236, and 09439, respectively, with a tenfold cross-validation procedure, and are 09312, 09178, 09324, 09413, and 09615, respectively, with a LOO scheme Both the accuracy and AUC values show that the proposed SRN feature selection method has the best performance In addition to the accuracy and the AUC, we also measured the significance between the two best feature selection methods, ie, the SVM-RFE (mRMR) and the proposed SRN method Experimental results show that the proposed SRN method is significantly more accurate than the SVM-RFE (mRMR) (p = 0011)

A Direct Image Contrast Enhancement Algorithm in the Wavelet Domain for Screening Mammograms

Abstract: In breast cancer diagnosis, the radiologists mainly use their eyes to discern cancer when they screen the mammograms. However, in many cases, cancer is not easily detected by the eyes because of the bad imaging conditions. In order to improve the correct diagnosis rate of cancer, image-enhancement technology is often used to enhance the image and aid the radiologists. In this paper, we develop a new image-enhancement technology in the wavelet domain for radiologists to screen mammograms. The new image-enhancement algorithm has several advantages. First, the proposed image-enhancement technology modifies a multiscale measure which matches the human vision system and thus the enhanced images have better visual quality; second, the image enhancement is accomplished in the wavelet domain and thus it can save time if the image is compressed by wavelet transform based methods; third, the end users can adjust the enhancement by manipulating a single parameter. Experiments were performed on mammograms and the results are progressive.

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Face detection in color images

Abstract: Human face detection plays an important role in applications such as video surveillance, human computer interface, face recognition, and face image database management. We propose a face detection algorithm for color images in the presence of varying lighting conditions as well as complex backgrounds. Based on a novel lighting compensation technique and a nonlinear color transformation, our method detects skin regions over the entire image and then generates face candidates based on the spatial arrangement of these skin patches. The algorithm constructs eye, mouth, and boundary maps for verifying each face candidate. Experimental results demonstrate successful face detection over a wide range of facial variations in color, position, scale, orientation, 3D pose, and expression in images from several photo collections (both indoors and outdoors).

IEEE transactions on pattern analysis and machine intelligence

Abstract: In the real world, a realistic setting for computer vision or multimedia recognition problems is that we have some classes containing lots of training data and many classes contain a small amount of training data. Therefore, how to use frequent classes to help learning rare classes for which it is harder to collect the training data is an open question. Learning with Shared Information is an emerging topic in machine learning, computer vision and multimedia analysis. There are different level of components that can be shared during concept modeling and machine learning stages, such as sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, etc. Regarding the specific methods, multi-task learning, transfer learning and deep learning can be seen as using different strategies to share information. These learning with shared information methods are very effective in solving real-world large-scale problems. This special issue aims at gathering the recent advances in learning with shared information methods and their applications in computer vision and multimedia analysis. Both state-of-the-art works, as well as literature reviews, are welcome for submission. Papers addressing interesting real-world computer vision and multimedia applications are especially encouraged. Topics of interest include, but are not limited to: • Multi-task learning or transfer learning for large-scale computer vision and multimedia analysis • Deep learning for large-scale computer vision and multimedia analysis • Multi-modal approach for large-scale computer vision and multimedia analysis • Different sharing strategies, e.g., sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, • Real-world computer vision and multimedia applications based on learning with shared information, e.g., event detection, object recognition, object detection, action recognition, human head pose estimation, object tracking, location-based services, semantic indexing. • New datasets and metrics to evaluate the benefit of the proposed sharing ability for the specific computer vision or multimedia problem. • Survey papers regarding the topic of learning with shared information. Authors who are unsure whether their planned submission is in scope may contact the guest editors prior to the submission deadline with an abstract, in order to receive feedback.

Histopathological Image Analysis: A Review

Abstract: Over the past decade, dramatic increases in computational power and improvement in image analysis algorithms have allowed the development of powerful computer-assisted analytical approaches to radiological data. With the recent advent of whole slide digital scanners, tissue histopathology slides can now be digitized and stored in digital image form. Consequently, digitized tissue histopathology has now become amenable to the application of computerized image analysis and machine learning techniques. Analogous to the role of computer-assisted diagnosis (CAD) algorithms in medical imaging to complement the opinion of a radiologist, CAD algorithms have begun to be developed for disease detection, diagnosis, and prognosis prediction to complement the opinion of the pathologist. In this paper, we review the recent state of the art CAD technology for digitized histopathology. This paper also briefly describes the development and application of novel image analysis technology for a few specific histopathology related problems being pursued in the United States and Europe.