Gwo Giun Lee

On digital mammogram segmentation and microcalcification detection using multiresolution wavelet analysis

In this paper a multiresolution wavelet analysis (MWA) and nonstationary Gaussian Markov random field (GMRF) technique is introduced for the detection of microcalcifications with high accuracy. The hierarchical multiresolution wavelet information in conjunction with the contextual information of the images extracted from GMRF provides an efficient technique for microcalcification detection. A Bayesian learning paradigm realized via the expectation maximization (EM) algorithm was also introduced for edge detection or segmentation of mass regions recorded on the mammograms. The strength of the technique is in the effective utilization of the rich contextural information in the images considered. The effectiveness of the approach has been tested with a number of mammographic images for which the microcalcification detection algorithm achieved a sensitivity (true positive rate) of 94% and specificity (true negative rate) of 88%. Considerably good results were also obtained for the segmentation algorithm. In addition, the results for both the detected microcalcifications and the segmented mass regions were superimposed for an interesting case under the methods introduced.

Image Segmentation Using Multiresolution Wavelet Analysis and Expectation-Maximization (EM) Algorithm for Digital Mammography

This article presents a novel algorithm for image segmentation via the use of the multiresolution wavelet analysis and the expectation maximization (EM) algorithm. The development of a multiresolution wavelet feature extraction scheme is based on the Gaussian Markov random field (GMRF) assumption in mammographic image modeling. Mammographic images are hierarchically decomposed into different resolutions. In general, larger breast lesions are characterized by coarser resolutions, whereas higher resolutions show finer and more detailed anatomical structures. These hierarchical variations in the anatomical features displayed by multiresolution decomposition are further quantified through the application of the Gaussian Markov random field. Because of its uniqueness in locality, adaptive features based on the nonstationary assumption of GMRF are defined for each pixel of the mammogram. Fibroadenomas are then segmented via the fuzzy C‐means algorithm using these localized features. Subsequently, the segmentation results are further enhanced via the introduction of a maximum a posteriori (MAP) segmentation estimation scheme based on the Bayesian learning paradigm. Gibbs priors or Gibbs random fields have also been incorporated into the learning scheme of the present research with very effective outcomes. In this article, the EM algorithm for MAP estimation is formulated. The EM algorithm provides an iterative and computationally simple algorithm based on the incomplete data concept.

Algorithm/Architecture Co-Design of 3-D Spatio–Temporal Motion Estimation for Video Coding

This paper presents a new spatio-temporal motion estimation algorithm and its VLSI architecture for video coding based on algorithm and architecture co-design methodology. The algorithm consists of the new strategies of spatio-temporal motion vector prediction, modified one-at-a-time search scheme, and multiple update paths derived from optimization theory. The hardware specification is for high-definition video coding. We applied the ME algorithm to H.264 reference software. Our algorithm surpasses recently published research and achieves close performance to full search. The VLSI implementation proves the low cost feature of our algorithm. The algorithm and architecture co-design concept is highly emphasized in this paper. We provide some quantitative example to show the necessity of algorithm and architecture co-design

Algorithm/Architecture Co-Exploration of Visual Computing on Emergent Platforms: Overview and Future Prospects

Concurrently exploring both algorithmic and architectural optimizations is a new design paradigm. This survey paper addresses the latest research and future perspectives on the simultaneous development of video coding, processing, and computing algorithms with emerging platforms that have multiple cores and reconfigurable architecture. As the algorithms in forthcoming visual systems become increasingly complex, many applications must have different profiles with different levels of performance. Hence, with expectations that the visual experience in the future will become continuously better, it is critical that advanced platforms provide higher performance, better flexibility, and lower power consumption. To achieve these goals, algorithm and architecture co-design is significant for characterizing the algorithmic complexity used to optimize targeted architecture. This paper shows that seamless weaving of the development of previously autonomous visual computing algorithms and multicore or reconfigurable architectures will unavoidably become the leading trend in the future of video technology.Concurrently exploring both algorithmic and architectural optimizations is a new design paradigm. This survey paper addresses the latest research and future perspectives on the simultaneous development of video coding, processing, and computing algorithms with emerging platforms that have multiple cores and reconfigurable architecture. As the algorithms in forthcoming visual systems become increasingly complex, many applications must have different profiles with different levels of performance. Hence, with expectations that the visual experience in the future will become continuously better, it is critical that advanced platforms provide higher performance, better flexibility, and lower power consumption. To achieve these goals, algorithm and architecture co-design is significant for characterizing the algorithmic complexity used to optimize targeted architecture. This paper shows that seamless weaving of the development of previously autonomous visual computing algorithms and multicore or reconfigurable architectures will unavoidably become the leading trend in the future of video technology.

Quantitative analysis of intrinsic skin aging in dermal papillae by in vivo harmonic generation microscopy

Chronological skin aging is associated with flattening of the dermal-epidermal junction (DEJ), but to date no quantitative analysis focusing on the aging changes in the dermal papillae (DP) has been performed. The aim of the study is to determine the architectural changes and the collagen density related to chronological aging in the dermal papilla zone (DPZ) by in vivo harmonic generation microscopy (HGM) with a sub-femtoliter spatial resolution. We recruited 48 Asian subjects and obtained in vivo images on the sun-protected volar forearm. Six parameters were defined to quantify 3D morphological changes of the DPZ, which we analyzed both manually and computationally to study their correlation with age. The depth of DPZ, the average height of isolated DP, and the 3D interdigitation index decreased with age, while DP number density, DP volume, and the collagen density in DP remained constant over time. In vivo high-resolution HGM technology has uncovered chronological aging-related variations in DP, and sheds light on real-time quantitative skin fragility assessment and disease diagnostics based on collagen density and morphology.

An RVC dataflow description of the AVC Constrained Baseline Profile decoder

Video codec applications become more and more complex to design. To ease the description of such applications, MPEG creates a Framework called Reconfigurable Video Coding (RVC). All existing codecs in MPEG have a similar structure, they are based on a hybrid decoding structure and some of their part can be reused on other design. In RVC, the dataflow is expressed using a network of components also called Functional Units (FUs) interconnected by FIFOs. An FU, programmed in CAL Language, includes the processing and the internal states. This paper puts the focus on a parallel dataflow description of the most complex MPEG RVC decoder available called MPEG4-AVC Constrained Baseline Profile (CBP) decoder.

A motion-adaptive deinterlacer via hybrid motion detection and edge-pattern recognition

A novel motion-adaptive deinterlacing algorithm with edge-pattern recognition and hybrid motion detection is introduced. The great variety of video contents makes the processing of assorted motion, edges, textures, and the combination of them very difficult with a single algorithm. The edge-pattern recognition algorithm introduced in this paper exhibits the flexibility in processing both textures and edges which need to be separately accomplished by line average and edge-based line average before. Moreover, predicting the neighboring pixels for pattern analysis and interpolation further enhances the adaptability of the edge-pattern recognition unit when motion detection is incorporated. Our hybrid motion detection features accurate detection of fast and slow motion in interlaced video and also the motion with edges. Using only three fields for detection also renders higher temporal correlation for interpolation. The better performance of our deinterlacing algorithm with higher content-adaptability and less memory cost than the state-of-the-art 4-field motion detection algorithms can be seen from the subjective and objective experimental results of the CIF and PAL video sequences.

Automatic Cell Segmentation and Nuclear-to-Cytoplasmic Ratio Analysis for Third Harmonic Generated Microscopy Medical Images

Traditional biopsy procedures require invasive tissue removal from a living subject, followed by time-consuming and complicated processes, so noninvasive in vivo virtual biopsy, which possesses the ability to obtain exhaustive tissue images without removing tissues, is highly desired. Some sets of in vivo virtual biopsy images provided by healthy volunteers were processed by the proposed cell segmentation approach, which is based on the watershed-based approach and the concept of convergence index filter for automatic cell segmentation. Experimental results suggest that the proposed algorithm not only reveals high accuracy for cell segmentation but also has dramatic potential for noninvasive analysis of cell nuclear-to-cytoplasmic ratio (NC ratio), which is important in identifying or detecting early symptoms of diseases with abnormal NC ratios, such as skin cancers during clinical diagnosis via medical imaging analysis.

Bi-Directional Trajectory Tracking With Variable Block-Size Motion Estimation for Frame Rate Up-Convertor

This paper presents bi-directional trajectory tracking with variable block-size motion estimation for frame rate up-conversion (FRUC) based on the algorithm/architecture co-exploration (AAC) design methodology. Due to concurrent exploration in both algorithm and architecture domains, the designed system requires fewer computations and lowers hardware cost, but is capable of enhancing the accuracy of motion vectors (MVs) by allowing MV refinement from coarse-grained to fine-grained. In addition, a method that uses multiple block candidates tracked by bi-directional MVs for interpolation is also presented to improve visual quality. Benefiting from AAC, we can extract architectural information at algorithmic design phase to determine the most feasible architecture; then, the proposed algorithm can be mapped onto target platform smoothly. The proposed FRUC system, which is capable of converting the frame rate from 60 fps up to 120 fps at full HD (1920 × 1080) resolution, was successfully implemented and verified on a field-programmable gate array. This FRUC systems performance has not only been shown to surpass state-of-art alternatives in algorithmic performance, but its hardware cost is less than the comparable works described in the literature.

Quantifying Intrinsic Parallelism Using Linear Algebra for Algorithm/Architecture Coexploration

Degree of parallelism (DoP) is an essential complexity metric that characterizes the number of independent operation sets (IOSs) that can be concurrently executed within an algorithm. This paper presents a generic framework to identify IOSs and to quantify the DoP based on rank theorem in linear algebra. This framework is applied to extract algorithmic parallelisms at various granularities, namely, multigrain parallelism. Our parallelism is intrinsic and platform independent and can provide insights into architectural information, thus facilitating mapping onto generic platforms and early back annotation for modifying algorithms. It plays a significant role in the concurrent optimization of both algorithms and architectures, referred to as Algorithm/Architecture Coexploration (AAC), by trading off between the DoP and the number of operations (NoO). This paper reports three case studies for AAC. The case study on an IDCT reveals that our framework accurately quantifies the parallelism for mapping the algorithm onto generic platforms, including FPGA and multicore systems. The IDCT parallelized by our technique surpasses a conventional spectral parallelization. By exploiting fine-grain parallelism, this paper presents a better porting of a discrete wavelet transform (DWT) onto single instruction multiple data (SIMD) machines compared with a commercial compiler. A high-quality deinterlacer is implemented on a low-cost multicore platform for real-time high-definition applications by analyzing the multigrain parallelism. These case studies reveal the effectiveness of our parallel analysis framework which is applicable to generic systems. Compared with traditional graph traversal techniques, our linear algebraic approach impressively features low complexity and is practical for complicated algorithms.