Automated Kidney Stone Segmentation by Seed Pixel Region Growing Approach: Initial Implementation and Results

This research develop a computer aided diagnosis prototype for early detection of kidney stone. Once a kidney stone is diagnosed accurately, this will be useful for the patients to change their diet condition. The proposed approach is based on five stages which includes kidney image acquisition, pre-processing, segmentation, feature extraction and classification. The enhanced seed region growing segmentation depends on the extracted feature granularities. Noise may be visible and more prevalent in certain dimensions of an image, where this particular specific portion will be extracted. The segmentation process is based on the thresholds of the identified renal stone regions. The segmented stone size portion is classified based on rules; if the size is greater than 2mm, then the stone is at benign stage; if the size is greater than 5mm, then it is in malignant stage; if the size is lesser than 2mm, then this leads to absence of stone. The proposed work is implemented in MATLAB with the development of an initial prototype with the detection of stone accuracy of 92%. Based on the experimental analysis, texture feature, threshold intensity values and stone sizes are evaluated. This study will help the urologist to take decision whether there is a presence or absence of stone in early stage diagnosis and clinical decision-making.

Object Extraction in Cluttered Environments via a P300-Based IFCE

One of the fundamental issues for robot navigation is to extract an object of interest from an image. The biggest challenges for extracting objects of interest are how to use a machine to model the objects in which a human is interested and extract them quickly and reliably under varying illumination conditions. This article develops a novel method for segmenting an object of interest in a cluttered environment by combining a P300-based brain computer interface (BCI) and an improved fuzzy color extractor (IFCE). The induced P300 potential identifies the corresponding region of interest and obtains the target of interest for the IFCE. The classification results not only represent the human mind but also deliver the associated seed pixel and fuzzy parameters to extract the specific objects in which the human is interested. Then, the IFCE is used to extract the corresponding objects. The results show that the IFCE delivers better performance than the BP network or the traditional FCE. The use of a P300-based IFCE provides a reliable solution for assisting a computer in identifying an object of interest within images taken under varying illumination intensities.

REGION GROWING IMAGE SEGMENTATION ON LARGE DATASETS USING GPU

Image segmentation is an important image processing, and it seems everywhere if we want to analyze what inside the image. There are varieties of applications of image segmentation such as the field of filtering noise from image, medical imaging, and locating objects in satellite images and in automatic traffic control systems, machine vision in problem of feature extraction and in recognition. This paper focuses on accelerating the image segmentation mechanism using region growing algorithm inside GPU (Graphical Processing Unit). In region growing algorithm, an initial set of small areas are iteratively merged according to similarity constraints. We have started by choosing an arbitrary seed pixel and compare it with neighboring pixels. Region is grown from the seed pixel by adding in neighboring pixels that are similar, increasing the size of the region. When the growth of one region stops we simply choose another seed pixel which does not yet belong to any region and start again. This whole process is continued until all pixels belong to some region. If any of the segment makers has the fusion cost lower than the maximum fusion cost (a given threshold), it is selected to grow. Avoid information overlapping like two threads attempting to merge its segment with the same adjacent segment. Experiments have demonstrated that the proposed shape features do not imply in a significant change of the segmentation results, as long as the algorithm’s parameters are properly adjusted. Moreover, experiments for performance evaluation indicated the potential of using GPUs to accelerate this kind of application. For a simple hardware (GeForce 630M GT), the parallel algorithm reached a maximum speed up of approximately 20-30% for different datasets. Considering that segmentation is responsible for a significant portion of the execution time in many image analysis applications, especially in object-oriented analysis of remote sensing images, the experimentally observed acceleration values are significant. Two variants of PBF (Parallel Best Fitting) and PLMBF (Parallel Local Mutual Best Fitting) have been used to analyze the best merging cost of the two segments. It has been found that the PLMBF has been performed better than PBF. It should also be noted that these performance gains can be obtained with low investment in hardware, as GPUs with increasing processing power are currently available on the market at declining prices. The parallel computational scheme is well suited for cluster computing, leading to a good solution for segmenting very large data sets.

A Method for Extracting QR Code from Complex Background Based on Morphology

In this paper we propose a new QR code extracting method based on morphology. Most of the time, locating Finder patterns is a significant part of QR code extraction. On the basis of traditional Finder Pattern detection method which checks whether certain areas meet 1:1:3:1:1 in both vertical and horizontal directions, we further refine the true Finder Patterns from several candidate areas through acreage proportion and gravity center detection, so as to eliminate interference from complex background. After image segmentation and getting the true finder patterns, other than the traditional method such as edge detection, we introduce the algorithm of region growth, along with choosing one seed pixel from obtained finder patterns to roughly figure out the QR code area. Eventually, by combining corner detection and inverse perspective transformation, we accomplish the extraction of QR code. Experiment results show that this method has robust correction capability from complex background and QR code deformation.