Robust automated tumour segmentation on histological and immunohistochemical tissue images

Tissue microarray (TMA) is a high throughput analysis tool to identify new diagnostic and prognostic markers in human cancers. However, standard automated method in tumour detection on both routine histochemical and immunohistochemistry (IHC) images is under developed. This paper presents a robust automated tumour cell segmentation model which can be applied to both routine histochemical tissue slides and IHC slides and deal with finer pixel-based segmentation in comparison with blob or area based segmentation by existing approaches. The presented technique greatly improves the process of TMA construction and plays an important role in automated IHC quantification in biomarker analysis where excluding stroma areas is critical. With the finest pixel-based evaluation (instead of area-based or object-based), the experimental results show that the proposed method is able to achieve 80% accuracy and 78% accuracy in two different types of pathological virtual slides, i.e., routine histochemical H&E and IHC images, respectively. The presented technique greatly reduces labor-intensive workloads for pathologists and highly speeds up the process of TMA construction and provides a possibility for fully automated IHC quantification.

Texture profile analysis of bioadhesive polymeric semisolids: Mechanical characterization and investigation of interactions between formulation components

This study reports the use of texture profile analysis (TPA) to mechanically characterize polymeric, pharmaceutical semisolids containing at least one bioadhesive polymer and to determine interactions between formulation components. The hardness, adhesiveness, force per unit time required for compression (compressibility), and elasticity of polymeric, pharmaceutical semisolids containing polycarbophil (1 or 5% w/w), polyvinylpyrrolidone (3 or 5% w/w), and hydroxyethylcellulose (3, 5, or 10% w/w) in phosphate buffer (pH 6.8) were determined using a texture analyzer in the TPA mode (compression depth 15 mm, compression rate 8 mm s(-1) 15 s delay period). Increasing concentrations of polycarbophil, poly vinylpyrrolidone, and hydroxyethylcellulose significantly increased product hardness, adhesiveness, and compressibility but decreased product elasticity. Statistically, interactions between polymeric formulation components were observed within the experimental design and were probably due to relative differences in the physical states of polyvinylpyrrolidone and polycarbophil in the formulations, i.e., dispersed/dissolved and unswollen/swollen, respectively. Increased product hardness and compressibility were possibly due to the effects of hydroxyethylcellulose, polyvinylpyrrolidone, and polycarbophil on the viscosity of the formulations. Increased adhesiveness was related to the concentration and, more importantly, to the physical state of polycarbophil. Decreased product elasticity was due to the increased semisolid nature of the product. TPA is a rapid, straightforward analytical technique that may be applied to the mechanical characterization of polymeric, pharmaceutical semisolids. It provides a convenient means to rapidly identify physicochemical interactions between formulation components. (C) 1996 John Wiley & Sons, Inc.

Human figure segmentation using independent component analysis

In this paper, we present a Statistical Shape Model for Human Figure Segmentation in gait sequences. Point Distribution Models (PDM) generally use Principal Component analysis (PCA) to describe the main directions of variation in the training set. However, PCA assumes a number of restrictions on the data that do not always hold. In this work, we explore the potential of Independent Component Analysis (ICA) as an alternative shape decomposition to the PDM-based Human Figure Segmentation. The shape model obtained enables accurate estimation of human figures despite segmentation errors in the input silhouettes and has really good convergence qualities.

Real-time GPU color-based segmentation of football players

In this paper, we propose a multi-camera application capable of processing high resolution images and extracting features based on colors patterns over graphic processing units (GPU). The goal is to work in real time under the uncontrolled environment of a sport event like a football match. Since football players are composed for diverse and complex color patterns, a Gaussian Mixture Models (GMM) is applied as segmentation paradigm, in order to analyze sport live images and video. Optimization techniques have also been applied over the C++ implementation using profiling tools focused on high performance. Time consuming tasks were implemented over NVIDIA's CUDA platform, and later restructured and enhanced, speeding up the whole process significantly. Our resulting code is around 4-11 times faster on a low cost GPU than a highly optimized C++ version on a central processing unit (CPU) over the same data. Real time has been obtained processing until 64 frames per second. An important conclusion derived from our study is the scalability of the application to the number of cores on the GPU. © 2011 Springer-Verlag.

Mean shift based gradient vector flow for image segmentation

In recent years, gradient vector flow (GVF) based algorithms have been successfully used to segment a variety of 2-D and 3-D imagery. However, due to the compromise of internal and external energy forces within the resulting partial differential equations, these methods may lead to biased segmentation results. In this paper, we propose MSGVF, a mean shift based GVF segmentation algorithm that can successfully locate the correct borders. MSGVF is developed so that when the contour reaches equilibrium, the various forces resulting from the different energy terms are balanced. In addition, the smoothness constraint of image pixels is kept so that over- or under-segmentation can be reduced. Experimental results on publicly accessible datasets of dermoscopic and optic disc images demonstrate that the proposed method effectively detects the borders of the objects of interest.

Anticipatory Planning Reveals Segmentation of Cortical Motor Output During Action Observation

It has been argued that the variation in brain activity that occurs when observing another person reflects a representation of actions that is indivisible, and which plays out in full once the intent of the actor can be discerned. We used transcranial magnetic stimulation to probe the excitability of corticospinal projections to 2 intrinsic hand muscles while motions to reach and grasp an object were observed. A symbolic cue either faithfully indicated the required final orientation of the object and thus the nature of the grasp that was required, or was in conflict with the movement subsequently displayed. When the cue was veridical, modulation of excitability was in accordance with the functional role of the muscles in the action observed. If however the cue had indicated that the alternative grasp would be required, modulation of output to first dorsal interosseus was consistent with the action specified, rather than the action observed-until the terminal phase of the motion sequence during which the object was seen lifted. Modulation of corticospinal output during observation is thus segmented-it progresses initially in accordance with the action anticipated, and if discrepancies are revealed by visual input, coincides thereafter with that of the action seen.

Using entropy based mean shift filter and modified watershed transform for grain segmentation

Life science research aims to continuously improve the quality and standard of human life. One of the major challenges in this area is to maintain food safety and security. A number of image processing techniques have been used to investigate the quality of food products. In this paper,we propose a new algorithm to effectively segment connected grains so that each of them can be inspected in a later processing stage. One family of the existing segmentation methods is based on the idea of watersheding, and it has shown promising results in practice.However,due to the over-segmentation issue,this technique has experienced poor performance in various applications,such as inhomogeneous background and connected targets. To solve this problem,we present a combination of two classical techniques to handle this issue.In the first step,a mean shift filter is used to eliminate the inhomogeneous background, where entropy is used to be a converging criterion. Secondly,a color gradient algorithm is used in order to detect the most significant edges, and a marked watershed transform is applied to segment cluttered objects out of the previous processing stages. The proposed framework is capable of compromising among execution time, usability, efficiency and segmentation outcome in analyzing ring die pellets. The experimental results demonstrate that the proposed approach is effectiveness and robust.