An Efficient Algorithm for Earth Surface Interpretation from Satellite Imagery

Many image segmentation algorithms are available but most of them are not fit for interpretation of satellite images. Mean-shift algorithm has been used in many recent researches as a promising image segmentation technique, which has the speed at O(kn2) where n is the number of data points and k is the number of average iteration steps for each data point. This method computes using a brute-force in the iteration of a pixel to compare with the region it is in. This paper proposes a novel algorithm named First-order Neighborhood Mean-shift (FNM) segmentation, which is enhanced from Mean-shift segmentation. This algorithm provides information about the relationship of a pixel with its neighbors; and makes them fall into the same region which improve the speed to O(kn). In this experiment, FNM were compared to well-known algorithms, i.e., K-mean (KM), Constrained K-mean (CKM), Adaptive K-mean (AKM), Fuzzy C-mean (FCM) and Mean-shift (MS) using the reference map from Landsat. FNM provided better results in terms of overall error and correctness criteria

Fully Automatic Ultrasound Fetal Heart Image Detection and Segmentation based on Texture Analysis

Ultrasound fetal heart image analysis is important for the antenatal diagnosis of congenital heart disease, therefore, design an automated fetal heart ultrasound image analysis approaches to improve detection ratio of congenital heart disease is necessary. Nevertheless, because of the complicated structure of fetal heart ultrasound image, location, detection and segmentation approaches of fetal heart images as interesting topics that get more attention. Therefore, in this work, we present a framework to segment ultrasound image automatically for tracking the boundary of fetal heart region. In the first step, this paper contributes to breed candidate regions. And then, in the segmentation progress, we apply an energy-based active contour model to detect the edges of fetal heart. Finally, in the experiment section, the performance is estimated by the Dice similarity coefficient, which calculate the spatial overlap between two different segmentation regions, and the experiment results indicate that the proposed algorithm achieves high levels of accuracy

A highly accurate level set approach for segmenting green microalgae images

We present a method for segmenting 2D microscopy images of freshwater green microalgae. Our approach is based on a specialized level set method, leading to efficient and highly accurate algae segmentation. The level set formulation of our problem allows us to generate an algae’s boundary curve as the result of an evolving level curve, based on computed background and algae regions in a given image. By characterizing the distributions of image intensity values in local regions, we are able to automatically classify image regions into background and algae regions. We present results obtained with our method. These results are very promising as they document that we can achieve highly accurate algae segmentations when comparing ours against manually segmented images (segmented by an expert biologist) and with results derived by other approaches covered in the literature.State of Sao Paulo Research Funding Agency (FAPESP) (procs. 2011/22749-8, 2012/00269-7, 2013/26647-0)Brazilian Federal Funding Agency (CNPq) (proc. 305696/2013-0

Segmentation of MRI Prostate Images

In this work, we investigate the performance of two segmentation methods; level set, and texture-based, in segmentation of prostate region. Both segmentation methods are applied onto transverse view of T2-W-MRI slice of prostate acquired using a 3T scanner. Level set method is one of the popular partial differential equations (PDEs) based in image processing especially in image segmentation as it relies on an initial value PDEs for a propagating level set function. “It also has been introduced in many disciplines, such as computer graphics, computational geometry, and optimization because this method acts as a tool for numerical analysis of surfaces and shapes. Besides, level set method can perform numerical computations involving curves and surfaces on a fixed Cartesian grid without having to parameterize the object. Prostate gland in MRI images is categorized as a texture image because the structures are not homogeneous and its surface has grey level values close to the neighbouring organs around the prostate which making it more difficult to detect the damaged tissues