Dae Hoe Kim

Mass type-specific sparse representation for mass classification in computer-aided detection on mammograms.

BackgroundBreast cancer is the leading cause of both incidence and mortality in women population. For this reason, much research effort has been devoted to develop Computer-Aided Detection (CAD) systems for early detection of the breast cancers on mammograms. In this paper, we propose a new and novel dictionary configuration underpinning sparse representation based classification (SRC). The key idea of the proposed algorithm is to improve the sparsity in terms of mass margins for the purpose of improving classification performance in CAD systems.MethodsThe aim of the proposed SRC framework is to construct separate dictionaries according to the types of mass margins. The underlying idea behind our method is that the separated dictionaries can enhance the sparsity of mass class (true-positive), leading to an improved performance for differentiating mammographic masses from normal tissues (false-positive). When a mass sample is given for classification, the sparse solutions based on corresponding dictionaries are separately solved and combined at score level. Experiments have been performed on both database (DB) named as Digital Database for Screening Mammography (DDSM) and clinical Full Field Digital Mammogram (FFDM) DBs. In our experiments, sparsity concentration in the true class (SCTC) and area under the Receiver operating characteristic (ROC) curve (AUC) were measured for the comparison between the proposed method and a conventional single dictionary based approach. In addition, a support vector machine (SVM) was used for comparing our method with state-of-the-arts classifier extensively used for mass classification.ResultsComparing with the conventional single dictionary configuration, the proposed approach is able to improve SCTC of up to 13.9% and 23.6% on DDSM and FFDM DBs, respectively. Moreover, the proposed method is able to improve AUC with 8.2% and 22.1% on DDSM and FFDM DBs, respectively. Comparing to SVM classifier, the proposed method improves AUC with 2.9% and 11.6% on DDSM and FFDM DBs, respectively.ConclusionsThe proposed dictionary configuration is found to well improve the sparsity of dictionaries, resulting in an enhanced classification performance. Moreover, the results show that the proposed method is better than conventional SVM classifier for classifying breast masses subject to various margins from normal tissues.

Multi-Objective based Spatio-Temporal Feature Representation Learning Robust to Expression Intensity Variations for Facial Expression Recognition

Facial expression recognition (FER) is increasingly gaining importance in various emerging affective computing applications. In practice, achieving accurate FER is challenging due to the large amount of inter-personal variations such as expression intensity variations. In this paper, we propose a new spatio-temporal feature representation learning for FER that is robust to expression intensity variations. The proposed method utilizes representative expression-states (e.g., onset, apex and offset of expressions) which can be specified in facial sequences regardless of the expression intensity. The characteristics of facial expressions are encoded in two parts in this paper. As the first part, spatial image characteristics of the representative expression-state frames are learned via a convolutional neural network. Five objective terms are proposed to improve the expression class separability of the spatial feature representation. In the second part, temporal characteristics of the spatial feature representation in the first part are learned with a long short-term memory of the facial expression. Comprehensive experiments have been conducted on a deliberate expression dataset (MMI) and a spontaneous micro-expression dataset (CASME II). Experimental results showed that the proposed method achieved higher recognition rates in both datasets compared to the state-of-the-art methods.

Computer-aided detection (CAD) of breast masses in mammography: combined detection and ensemble classification

We propose a novel computer-aided detection (CAD) framework of breast masses in mammography. To increase detection sensitivity for various types of mammographic masses, we propose the combined use of different detection algorithms. In particular, we develop a region-of-interest combination mechanism that integrates detection information gained from unsupervised and supervised detection algorithms. Also, to significantly reduce the number of false-positive (FP) detections, the new ensemble classification algorithm is developed. Extensive experiments have been conducted on a benchmark mammogram database. Results show that our combined detection approach can considerably improve the detection sensitivity with a small loss of FP rate, compared to representative detection algorithms previously developed for mammographic CAD systems. The proposed ensemble classification solution also has a dramatic impact on the reduction of FP detections; as much as 70% (from 15 to 4.5 per image) at only cost of 4.6% sensitivity loss (from 90.0% to 85.4%). Moreover, our proposed CAD method performs as well or better (70.7% and 80.0% per 1.5 and 3.5 FPs per image respectively) than the results of mammography CAD algorithms previously reported in the literature.

Breast mass detection using slice conspicuity in 3D reconstructed digital breast volumes

In digital breast tomosynthesis, the three dimensional (3D) reconstructed volumes only provide quasi-3D structure information with limited resolution along the depth direction due to insufficient sampling in depth direction and the limited angular range. The limitation could seriously hamper the conventional 3D image analysis techniques for detecting masses because the limited number of projection views causes blurring in the out-of-focus planes. In this paper, we propose a novel mass detection approach using slice conspicuity in the 3D reconstructed digital breast volumes to overcome the above limitation. First, to overcome the limited resolution along the depth direction, we detect regions of interest (ROIs) on each reconstructed slice and separately utilize the depth directional information to combine the ROIs effectively. Furthermore, we measure the blurriness of each slice for resolving the degradation of performance caused by the blur in the out-of-focus plane. Finally, mass features are extracted from the selected in focus slices and analyzed by a support vector machine classifier to reduce the false positives. Comparative experiments have been conducted on a clinical data set. Experimental results demonstrate that the proposed approach outperforms the conventional 3D approach by achieving a high sensitivity with a small number of false positives.

Region based stellate features for classification of mammographic spiculated lesions in computer-aided detection

In this paper, new region-based stellate features have been developed for correctly differentiating spiculated malignant lesions from normal tissues in mammography. The purpose of using proposed features is to reduce the number of false positive that are produced during the detection of suspicious regions in computeraided detection (CAD). It has been well-known that one particularly important characteristic of spiculated lesions is that they have usually radiating patterns of linear spicules. Based on the aforementioned observation, we propose effective region-based stellate features, designed for well representing the stellate pattern information within a given region-of-interest (ROI). In particular, the proposed features are calculated using statistical information of the stellate patterns within local regions of a given ROI. The effectiveness of our stellate features has been successfully tested on two public mammogram databases (DBs).

Combining multiresolution local binary pattern texture analysis and variable selection strategy applied to computer-aided detection of breast masses on mammograms

In this paper, we propose new texture features, so-called multiresolution local binary pattern (LBP), for classifying breast masses and normal tissue on mammograms. The proposed texture features are able to well characterize the texture patterns of mass core region and its margin. To this end, two individual LBP patterns are extracted from the core region and the ribbon region of pixels of a given mass region, respectively. Further, in order to improve classification accuracy, SVM-RFE based variable selection solution is applied for selecting an optimal subset of variables of multiresolution LBP texture features. Extensive and comparative experiments have been conducted to evaluate our multiresolution LBP features in conjunction with SVM-RFE based variable selection on public benchmark mammogram data set. Our results demonstrate the feasibility of combining our multiresolution LBP features with variable selection strategy for classification of masses and normal tissue on mammograms.

Mammographic enhancement with combining local statistical measures and sliding band filter for improved mass segmentation in mammograms

In this study, a novel mammogram enhancement solution is proposed, aiming to improve the quality of subsequent mass segmentation in mammograms. It has been widely accepted that characteristics of masses are usually hyper-dense or uniform density with respect to its background. Also, their core parts are likely to have high-intensity values while the values of intensity tend to be decreased as the distance to core parts increases. Based on the aforementioned observations, we develop a new and effective mammogram enhancement method by combining local statistical measurements and Sliding Band Filtering (SBF). By effectively combining local statistical measurements and SBF, we are able to improve the contrast of the bright and smooth regions (which represent potential mass regions), as well as, at the same time, the regions where their surrounding gradients are converging to the centers of regions of interest. In this study, 89 mammograms were collected from the public MAIS database (DB) to demonstrate the effectiveness of the proposed enhancement solution in terms of improving mass segmentation. As for a segmentation method, widely used contour-based segmentation approach was employed. The contour-based method in conjunction with the proposed enhancement solution achieved overall detection accuracy of 92.4% with a total of 85 correct cases. On the other hand, without using our enhancement solution, overall detection accuracy of the contour-based method was only 78.3%. In addition, experimental results demonstrated the feasibility of our enhancement solution for the purpose of improving detection accuracy on mammograms containing dense parenchymal patterns.

Micro-Expression Recognition with Expression-State Constrained Spatio-Temporal Feature Representations

Recognizing spontaneous micro-expression in video sequences is a challenging problem. In this paper, we propose a new method of small scale spatio-temporal feature learning. The proposed learning method consists of two parts. First, the spatial features of micro-expressions at different expression-states (i.e., onset, onset to apex transition, apex, apex to offset transition and offset) are encoded using convolutional neural networks (CNN). The expression-states are taken into account in the objective functions, to improve the expression class separability of the learned feature representation. Next, the learned spatial features with expression-state constraints are transferred to learn temporal features of micro-expression. The temporal feature learning encodes the temporal characteristics of the different states of the micro-expression using long short-term memory (LSTM) recurrent neural networks. Extensive and comprehensive experiments have been conducted on the publically available CASME II micro-expression dataset. The experimental results showed that the proposed method outperformed state-of-the-art micro-expression recognition methods in terms of recognition accuracy.

A deep facial landmarks detection with facial contour and facial components constraint

In this paper, we propose a new facial landmarks detection method based on deep learning with facial contour and facial components constraints. The proposed deep convolutional neural networks (DCNNs) for facial landmark detection consists of two deep networks: one DCNN is to detect landmarks constrained on the facial contour and the other is to detect landmarks constrained on facial components. A novel DCNN structure for the landmarks detection with facial component constraints is proposed, which branches the network at higher layers in order to capture the intricate local facial components features. Moreover, a novel learning strategy is proposed to learn the DCNN for detecting the landmarks on the facial contour by exploiting the relationship between facial contour landmarks and those on facial components. Experimental results have shown that the proposed method outperforms the state-of-the-art FLD methods.

Spatio-temporal representation for face authentication by using multi-task learning with human attributes

For human identification, facial motion is useful in representing specific dynamic signature. In this paper, we present an effective spatio-temporal representation from facial motion as well as appearance by devising a 3D convolutional neural network (CNN). To maintain the intra-class invariance with limited number of training samples, a multi-task learning approach with human attributes, which are high-level semantic descriptions for identity, has been proposed. Identity-related human attributes can be leveraged to learn the 3D CNN. Comparative experiment has showed that the proposed method improves the performance of the face-based authentication system compared to conventional methods by effectively encoding facial appearance and motions with identity-related human attributes.