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Enhanced random forest for mitosis detection 14

Enhanced random forest for mitosis detection

引用

9th indian conference on computer vision, graphics and image processing, ICVGIP 2014

作者： Paul, Angshuman Mukherjee, Dipti Prasad Indian Statistical Institute 203 B. T. Road Kolkata700108 India

ISBN: (纸本)1595930361

Histopathological grading of cancer is a measure of the cell appearance in malignant neoplasms. Grading offers an in-sight to the growth of the cancer and helps in developing individual treatment plans. The Nottingham grading system [12], well known method for invasive breast cancer grading, primarily relies on the mitosis count in histopathological slides. Pathologists manually identify mitotic figures from a few thousand slide images for each patient to determine the grade of the cancer. Mitotic figures are hard to identify as the appearance of the mitotic cells change at different phases of mitosis. So, the manual cancer grading is not only a tedious job but also prone to observer variability. We propose a fast and accurate approach for automatic mitosis detection from histopathological images using an enhanced random forest classifier with weighted random trees. The random trees are assigned a tree penalty and a forest penalty depending on their classification performance at the training phase. The weight of a tree is calculated based on these penalties. The forest is trained through regeneration of population from weighted trees. The input data is classified based on weighted voting from the random trees after several populations. Experiments show at least 11 percent improvement in F1 score on more than 450 histopathological images at ×40 magnification. Copyright 2014 ACM.

关键词： Grading

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Multi-criteria energy minimization with boundedness, edge-density and rarity, for object saliency in natural images 14

Multi-criteria energy minimization with boundedness, edge-de...

引用

9th indian conference on computer vision, graphics and image processing, ICVGIP 2014

作者： Roy, Sudeshna Das, Sukhendu Indian Institute Technology Madras India

ISBN: (纸本)1595930361

Recent methods of bottom-up salient object detection have attempted to either: (i) obtain a probability map with a 'contrast rarity' based functional, formed using low level cues;or (ii) Minimize an objective function, to detect the object. Most of these methods fail for complex, natural scenes, such as the PASCAL-VOC challenge dataset which contains images with diverse appearances, illumination conditions, multiple distracting objects and varying scene environments. We thus formulate a novel multi-criteria objective function which captures many dependencies and the scene structure for correct spatial propagation of low-level priors to perform salient object segmentation, in such cases. Our proposed formulation is based on CRF modeling where the minimization is performed using graph cut and the optimal parameters of the objective function are learned using a max-margin framework from the training set, without the use of class labels. Hence the method proposed is unsu-pervised, and works efficiently when compared to the very recent state-of-the art methods of saliency map detection and object proposals. Results, compared using F-measure and intersection-over-union scores, show that the proposed method exhibits superior performance in case of the complex PASCAL-VOC 2012 object segmentation dataset as well as the traditional MSRA-B saliency dataset. Copyright 2014 ACM.

关键词： Object detection

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3D visual speech animation from image sequences 14

3D visual speech animation from image sequences

引用

9th indian conference on computer vision, graphics and image processing, ICVGIP 2014

作者： Musti, Utpala Ouni, Slim Zhou, Ziheng Pietikäinen, Matti Center for Machine Vision Research University of Oulu Finland Université de Lorraine LORIA UMR 7503 France

ISBN: (纸本)1595930361

In this paper we describe an early version of our system which synthesizes 3D visual speech including tongue and teeth from frontal facial image sequences. This system is developed for 3D Visual Speech Animation (VSA) using images generated by an existing state-of-the-art image-based VSA system. In fact, the prime motivation for this system is to have a 3D VSA system from limited amount of training data when compared to that required for developing a conventional corpus based 3D VSA system. It consists of two modules. The first module iteratively estimates the 3D shape of the external facial surface for each image in the input sequence. The second module complements the external face with 3D tongue and teeth to complete the perceptually crucial visual speech information. This has the added advantages of 3D visual speech, which are renderability of the face in different poses and illumination conditions and, enhanced visual information of tongue and teeth. The first module for 3D shape estimation is based on the detection of facial landmarks in images. It uses a prior 3D Morphable Model (3D-MM) trained using 3D facial data. For the time being it is developed for a person-specific domain, i.e., the 3D-MM and the 2D facial landmark detector are trained using the data of a single person and tested with the same person-specific data. The estimated 3D shape sequences are provided as input to the second module along with the phonetic segmentation. For any particular 3D shape, tongue and teeth information is generated by rotating the lower jaw based on few skin points on the jaw and animating a rigid 3D tongue through keyframe interpolation. Copyright 2014 ACM.

关键词： Animation

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A novel approach for computer aided diagnosis of schizophrenia using auditory oddball functional MRI 14

A novel approach for computer aided diagnosis of schizophren...

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9th indian conference on computer vision, graphics and image processing, ICVGIP 2014

作者： Juneja, Akanksha Rana, Bharti Agrawal, R.K. School of Computer and Systems Sciences Jawaharlal Nehru University New Delhi India

ISBN: (纸本)1595930361

Schizophrenia is a serious mental illness that requires timely and accurate diagnosis. Functional magnetic resonance imaging (fMRI) helps in identifying variations in activation patterns of schizophrenia patients and healthy subjects. But, manual diagnosis using fMRI is cumbersome and prone to subjective errors. This has drawn the attention of pattern recognition and computer vision research community towards developing a reliable and efficient decision model for computer aided diagnosis (CAD) of schizophrenia. However, high dimensionality and limited availability of fMRI samples leads to curse-of-dimensionality which may deteriorate the performance of a decision model. In this research work, a combination of feature extraction and feature selection techniques is employed to obtain a reduced set of relevant features for differentiating schizophrenia patients from healthy subjects. A general linear model approach is used for feature extraction on pre-processed fMRI data. Further t-test based feature selection is employed to determine a subset of discriminative features which are used for learning a decision model using support vector machine. Experiments are carried out on two balanced and well-age matched datasets (acquired on 1.5 Tesla and 3 Tesla scanners) of auditory odd-ball task derived from a publicly available multisite FBIRN dataset. The performance is evaluated in terms of sensitivity, specificity and classification accuracy, and compared with two well-known existing approaches. Experimental results demonstrate that the proposed model outperforms the two existing approaches in terms of sensitivity, specificity and classification accuracy. With the proposed approach, the classification accuracy of 80.9% and 88.0% is achieved for 1.5 Tesla and 3 Tesla datasets respectively. In addition, the brain regions containing the discriminative features are identified which may be used as biomarkers for CAD of schizophrenia using fMRI. Copyright 2014 ACM.

关键词： computer aided diagnosis

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Localization of curved scene text in video based on Fuzzy Curve Tracing of extracted planar surface 14

Localization of curved scene text in video based on Fuzzy Cu...

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9th indian conference on computer vision, graphics and image processing, ICVGIP 2014

作者： Boaz, Too Kipyego Prabhakar, C.J. Department of Computer Science Kuvempu University Shankaraghatta Shivamogga Karnataka577 415 India

ISBN: (纸本)1595930361

In this paper, we present a novel technique to localize curved multi-script text contained in natural scene video based on Fuzzy Curve Tracing (FCT) of extracted planar surface. In order to read and interpret easily, text information is usually written on planar surfaces, for instance billboards, walls of buildings, road-signs and banners. This motivated us to detect planar surfaces by fitting a planar model, that is constructed using Random Sample Consensus (RANSAC). It is assumed that the detected planar surface contains text and is segmental from background using Graph Cuts through Markov Random Field (MRF) labeling of pixels belongs to planar surface. Within the extracted planar surface, the curved text is detected using fuzzy curve tracing, which traces and generates curve path of the text by establishing spatial relations among the cluster centers identified through fuzzy c-means clustering of character regions. Finally, curved text is localized by identifying character regions wherever generated curve path pass through it. The experimental results are evaluated for text localization using recall, precision and f-measure. Based on these metrics result, it's incontestible that the projected technique outperforms the popular existing methods. Copyright 2014 ACM.

关键词： Markov processes

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Complementary Cumulative Precision Distribution: A New Graphical Metric for Medical image Retrieval System

Complementary Cumulative Precision Distribution: A New Graph...

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conference on Medical Imaging - image Perception, Observer Performance, and Technology Assessment

作者： Dash, Jatindra Kumar Mukhopadhyay, Sudipta Khandelwal, Niranjan Indian Inst Technol Dept Elect & Elect Commun Engn Kharagpur 721302 W Bengal India Post Grad Inst Med Educ & Res Chandigarh India

ISBN: (纸本)9780819498304

Several single valued measures have been proposed by researchers for the quantitative performance evaluation of medical image retrieval systems. Precision and recall are the most common evaluation measures used by researchers. Amongst graphical measures proposed, precision vs. recall graph is the most common evaluation measure. Precision vs. recall graph evaluates different systems by varying the operating points (number of top retrieval considered). However, in real life the operating point for different applications are known. Therefore, it is essential to evaluate different retrieval systems at a particular operating point set by the user. None of the graphical metric provides the variation of performance of query images over the entire database at a particular operating point. This paper proposes a graphical metric called Complementary Cumulative Precision Distribution (CCPD) that evaluates different systems at a particular operating point considering each images in the database for query. The strength of the metric is its ability to represent all these measures pictorially. The proposed metric (CCPD) pictorially represents the different possible values of precision and the fraction of query images at those precision values considering number of top retrievals constant. Different scalar measures are derived from the proposed graphical metric (CCPD) for effective evaluation of retrieval systems. It is also observed that the proposed metric can be used as a tie breaker when the performance of different methods are very close to each other in terms of average precision.

关键词： Content based image retrieval retrieval performance performance metric precision complementary cumulative precision distribution recall complementary cumulative recall distribution

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A Schrödinger formalism for simultaneously computing the Euclidean distance transform and its gradient density 14

A Schrödinger formalism for simultaneously computing the Eu...

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9th indian conference on computer vision, graphics and image processing, ICVGIP 2014

作者： Gurumoorthy, Karthik S. Rangarajan, Anand International Center for Theoretical Sciences Tata Institute of Fundamental Research Bangalore Karnataka India Department of Computer and Information Science and Engineering University of Florida GainesvilleFL United States

ISBN: (纸本)1595930361

In this work, we employ the well-known Hamilton-Jacobi to Schrödinger connection to present a unified framework for computing both the Euclidean distance function and its gradient density in two dimensions. Previous work in this direction considered two different formalisms for independently computing these quantities. While the two formalisms are very closely related, their lack of integration is theoretically troubling and practically cumbersome. We introduce a novel Schrödinger wave function for representing the Euclidean distance transform from a discrete set of points. An approximate distance transform is computed from the magnitude of the wave function while the gradient density is estimated from the Fourier transform of the phase of the wave function. In addition to its simplicity and efficient O(N logN) computation, we prove that the wave function-based density estimator increasingly, closely approximates the distance transform gradient density (as a free parameter approaches zero) with the added benefit of not requiring the true distance function. Copyright 2014 ACM.

关键词： Wave functions

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Message from the general chairs

ACM International Conference Proceeding Series

引用

ACM International conference Proceeding Series 2014年 14卷

作者： Ramakrishnan, A.G. Malik, Jitendra Indian Institute of Science Bangalore India University of California Berkeley United States

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Writer identification using a deep neural network 14

Writer identification using a deep neural network

引用

9th indian conference on computer vision, graphics and image processing, ICVGIP 2014

作者： Chu, Jun Srihari, Sargur Department of Computer Science and Engineering University at Buffalo State University of New York BuffaloNY14269 United States

ISBN: (纸本)1595930361

Most work on automatic writer identification relies on hand-writing features defined by humans[6, 4]. These features correspond to basic units such as letters and words of text. Instead of relying on human-defined features, we consider here the determination of writing similarity using automatically determined word-level features learnt by a deep neural network. We generalize the problem of writer identification to the definition of a content-irrelevant handwriting similarity. Our method first takes whether two words were written by the same person as a discriminative label for word-level feature training. Then, based on word-level features, we define writing similarity between passages. This similarity not only shows the distinction between writing styles of different people, but also the development of style of the same person. Performance with several hidden layers in the neural network are evaluated. The method is applied to determine how a person's writing style changes with time considering a children's writing dataset. The children's handwriting data are annually collected. They were written by children of 2nd, 3rd or 4th grade. Results are given with a whole passage (50 words) of writing over one-year change. As a comparison, similar experiments on a small amount of data using conventional generative model are also given. Copyright 2014 ACM.

关键词： Deep neural networks

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Emotion recognition from geometric facial features using self-organizing map

Emotion recognition from geometric facial features using sel...

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作者： Majumder, Anima Behera, Laxmidhar Subramanian, Venkatesh K. Department of Electrical Engineering Indian Institute of Technology Kanpur Kanpur 208016 India

This paper presents a novel emotion recognition model using the system identification approach. A comprehensive data driven model using an extended Kohonen self-organizing map (KSOM) has been developed whose input is a 26 dimensional facial geometric feature vector comprising eye, lip and eyebrow feature points. The analytical face model using this 26 dimensional geometric feature vector has been effectively used to describe the facial changes due to different expressions. This paper thus includes an automated generation scheme of this geometric facial feature vector. The proposed non-heuristic model has been developed using training data from MMI facial expression database. The emotion recognition accuracy of the proposed scheme has been compared with radial basis function network, multi-layered perceptron model and support vector machine based recognition schemes. The experimental results show that the proposed model is very efficient in recognizing six basic emotions while ensuring significant increase in average classification accuracy over radial basis function and multi-layered perceptron. It also shows that the average recognition rate of the proposed method is comparatively better than multi-class support vector machine. © 2013 Elsevier Ltd. All rights reserved.

关键词： Support vector machines

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