Most previous efforts in developing computer-aided detection (CADe) of colonic polyps apply similar measures or parameters to detect polyps regardless of their locations under an implicit assumption that all the polyps reside in a similar local environment, e.g. on a relatively flat colon wall. In reality, this implicit assumption is frequently invalid, because the haustral folds can have a very different local environment from that of the relatively flat colon wall. We conjecture that this assumption may be a major cause of missing the detection of polyps, especially small polyps (<10 mm linear size) located on the haustral folds. In this paper, we take the concept of adaptiveness and present an adaptive paradigm for CADe of colonic polyps. Firstly, we decompose the complicated colon structure into two simplified sub-structures, each of which has similar properties, of (1) relatively flat colon wall and (2) ridge-shaped haustral folds. Then we develop local environment descriptions to adaptively reflect each of these two simplified sub-structures. To show the impact of the adaptiveness of the local environment descriptions upon the polyp detection task, we focus on the local geometrical measures of the volume data for both the detection of initial polyp candidates (IPCs) and the reduction of false positives (FPs) in the IPC pool. The experimental outcome using the local geometrical measures is very impressive such that not only the previously-missed small polyps on the folds are detected, but also the previously miss-removed small polyps on the folds during FP reduction are retained. It is expected that this adaptive paradigm will have a great impact on detecting the small polyps, measuring their volumes and volume changes over time, and optimizing their management plan.

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An adaptive paradigm for computer-aided detection of colonic polyps.

In supervised feature selection methods, the features that will be used for classification are selected according to their relationship with label information used for teaching classifiers. In the presented study, this relationship was measured with two different correlation coefficients, one parametric and one nonparametric. Pearson was used as the parametric correlation coefficient and Kendall was used as the nonparametric correlation coefficient. Human skin segmentation in color images has been taken as a case study to demonstrate the advantages of parametric and nonparametric correlation coefficients. In the color images, when the skin segmentation is applied based on pixels, the problem turns into a binary classification problem. For each pixel, 72 features based on color and texture are extracted. According to the order of the metrics obtained, the features are sorted by high correlated to low correlated and the strongest features are selected for classification. The classifier is trained and tested by extracted features with and without feature selection methods obtained from the Pratheepan database. When the eliminated features are added again, it is seen that either its contribution is negligibly low or it disrupts the classification.

Parametric and nonparametric correlation ranking based supervised feature selection methods for skin segmentation

Graph based method for cell segmentation and detection in live-cell fluorescence microscope imaging

Micro-expressions are brief and subtle facial expressions that go on and off the face in a fraction of a second. This kind of facial expressions usually occurs in high stake situations and is considered to reflect a humans real intent. They have been studied to better understand non-verbal communications and in medical applications where is almost impossible to engage in a conversation or try to read the facial emotions or body language of a patient. There has been some interest works in micro-expression analysis, however, a great majority of these methods are based on classically established computer vision methods such as local binary patterns, histogram of gradients and optical flow. Considering the fact that this area of research is relatively new, much contributions remains to be made. In this thesis, we present a novel methodology for subtle motion and micro-expression analysis. We propose to use the Riesz pyramid, a multi-scale steerable Hilbert transformer which has been used for 2-D phase representation and video amplification, as the basis for our methodology. For the general subtle motion analysis step, we transform an image sequence with the Riesz pyramid, extract and filter the image phase variations as proxies for motion. Furthermore, we isolate regions of interest where subtle motion might take place and mask noisy areas by thresholding the local amplitude. The total sequence is transformed into a 1D signal which is used for temporal analysis and subtle motion spotting. We create our own database of subtle motion sequences to test our method. For the micro-expression spotting step, we adapt the previous method to process some facial regions of interest. We also develop a heuristic method to detect facial micro-events that separates real micro-expressions from eye blinkings and subtle eye movements. For the micro-expression classification step, we exploit the dominant orientation constancy from the Riesz transform to average the micro-expression sequence into an image pair. Based on that, we introduce the Mean Oriented Riesz Feature descriptor. The accuracy of our methods are tested in two spontaneous micro-expressions databases. Furthermore, we analyse the parameter variations and their effect in our results.

Analysis of Micro-Expressions based on the Riesz Pyramid : Application to Spotting and Recognition

Detection and segmentation of faces from an image is a crucial problem that has gained importance, face detection and segmentation play the main role in the face recognition systems. There are many difficulties should be solved to make the face detection and segmentation algorithm successful. The face skin has special colors range as well as special textures that can be detected by using texture recognition algorithms which recognized skin from the background. In this paper we introduced a new method for face detection and segmentation based on face color, we uses the YCbCr color space as a method to segment image to many regions. Gray level co-occurrence matrix used to extract the important features represent the skin, and then Tamura texture used to remove all the non skin blobs which is recognized as skin by GLCM. The proposed algorithm tested with many images and it was successfully recognize the images with faces from images without faces. The proposed algorithm has high efficient in detected faces and segmented faces from the background. The accuracy of this algorithm more than 99% in detection faces and also segment its. The proposed algorithm forms a prerequisite for any practical verification system using face as the main attribute.

/pdf/detection-and-segmentation-of-human-face-49wi9oyw2f.pdf

Azeez Qazzaz

Papers

Detection and Segmentation of Human Face