Improvement of Single Seeded Region Growing Algorithm on Image Segmentation

To form a hybrid approach for image segmentation, several researches have been done to combine some techniques for better improvements. This article is concerned with image segmentation using combined methods. To separate foreground from background in image the pixel intensities have been considered. For image segmentation region growing with seed pixel is one of the most important segmentation methods. In single seeded region growing, it is very difficult to find out the proper position of the pixel during the selection. By considering the limitation of single seeded region growing an improved algorithm for region growing has proposed. The position of the seed pixel can be chosen before growing the region for segmentation using the proposed technique. Then combine this method with existing single seeded region growing algorithm. After the comparison using segmentation evaluation parameters it can be seen that, this combined method works better than others existing methods

Image segmentation with scalable spatial information

A general approach is proposed for the design of image segmentation algorithms utilizing spatial information which is the combined properties of a collection of neighborhood pixels. With different types of properties and different number of neighborhood pixels being utilized, segmentation algorithms with different speed and accuracy performance can be designed. Six algorithms have been implemented with their performance investigated and compared.published_or_final_versio

DEWA: A Multiaspect Approach for Multiple Face Detection in Complex Scene Digital Image

A new approach for detecting faces in a digital image with unconstrained background has been developed. The approach is composed of three phases: segmentation phase, filtering phase and localization phase. In the segmentation phase, we utilized both training and non-training methods, which are implemented in user selectable color space. In the filtering phase, Minkowski addition-based objects removal has been used for image cleaning. In the last phase, an image processing method and a data mining method are employed for grouping and localizing objects, combined with geometric-based image analysis. Several experiments have been conducted using our special face database that consists of simple objects and complex objects. The experiment results demonstrated that the detection accuracy is around 90% and the detection speed is less than 1 second in average

A hybrid method for traumatic brain injury lesion segmentation

Traumatic brain injuries are significant effects of disability and loss of life. Physicians employ computed tomography (CT) images to observe the trauma and measure its severity for diagnosis and treatment. Due to the overlap of hemorrhage and normal brain tissues, segmentation methods sometimes lead to false results. The study is more challenging to unitize the AI field to collect brain hemorrhage by involving patient datasets employing CT scans images. We propose a novel technique free-form object model for brain injury CT image segmentation based on superpixel image processing that uses CT to analyzing brain injuries, quite challenging to create a high outstanding simple linear iterative clustering (SLIC) method. The maintains a strategic distance of the segmentation image to reduced intensity boundaries. The segmentation image contains marked red hemorrhage to modify the free-form object model. The contour labelled by the red mark is the output from our free-form object model. We proposed a hybrid image segmentation approach based on the combined edge detection and dilation technique features. The approach diminishes computational costs, and the show accomplished 96.68% accuracy. The segmenting brain hemorrhage images are achieved in the clustered region to construct a free-form object model. The study also presents further directions on future research in this domain