Research on unstructured road detection algorithm based on improved morphological operations

<p><strong>Abstract.</strong> As the high resolution satellite images have become easily available, this has motivated researchers for searching advanced methods for object detection and extraction from satellite images. Roads are important curvilinear object as they are a used in urban planning, emergency response, route planning etc. Automatic road detection from satellite images has now become an important topic in photogrammetry with the advances in remote sensing technology. In this paper, a method for road detection and extraction of satellite images has been introduced. This method uses the concept of histogram equalization, Otsu's method of image segmentation, connected component analysis and morphological operations. The aim of this paper is to discover the potential of high resolution satellite images for detecting and extracting the road network in a robust manner.</p>

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Low and high grade glioma segmentation in multispectral brain MRI data

Acta Universitatis Sapientiae Informatica ◽

10.2478/ausi-2018-0007 ◽

2018 ◽

Vol 10 (1) ◽

pp. 110-132 ◽

Cited By ~ 8

Author(s):

László Szilágyi ◽

David Iclănzan ◽

Zoltán Kapás ◽

Zsófia Szabó ◽

Ágnes Győrfi ◽

...

Keyword(s):

Early Stage ◽

Brain Mri ◽

High Grade Glioma ◽

Detection Algorithm ◽

Gabor Wavelet ◽

Low Grade ◽

Data Sets ◽

Morphological Operations ◽

High Grade ◽

Mass Screening System

Abstract Several hundreds of thousand humans are diagnosed with brain cancer every year, and the majority dies within the next two years. The chances of survival could be easiest improved by early diagnosis. This is why there is a strong need for reliable algorithms that can detect the presence of gliomas in their early stage. While an automatic tumor detection algorithm can support a mass screening system, the precise segmentation of the tumor can assist medical staff at therapy planning and patient monitoring. This paper presents a random forest based procedure trained to segment gliomas in multispectral volumetric MRI records. Beside the four observed features, the proposed solution uses 100 further features extracted via morphological operations and Gabor wavelet filtering. A neighborhood-based post-processing was designed to regularize and improve the output of the classifier. The proposed algorithm was trained and tested separately with the 54 low-grade and 220 high-grade tumor volumes of the MICCAI BRATS 2016 training database. For both data sets, the achieved accuracy is characterized by an overall mean Dice score > 83%, sensitivity > 85%, and specificity > 98%. The proposed method is likely to detect all gliomas larger than 10 mL.

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A novel method to detect foreground region using morphological operations with block based enhancement for underwater images

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i3.13159 ◽

2018 ◽

Vol 7 (3) ◽

pp. 1751

Author(s):

M Sudhakar ◽

M Janaki Meena

Keyword(s):

Wavelet Transforms ◽

Detection Algorithm ◽

Discrete Wavelet ◽

Morphological Operations ◽

Underwater Image ◽

Foreground Region ◽

Fuzzy C Means Clustering ◽

Sobel Edge Detection ◽

Block Based ◽

Light Color

Automation of detecting the Foreground Region (FR) or Shape of the object is essential in several computer vision, object recognition applications and poses several challenges in case of underwater images. Although Synthetic Sonar Images produce better quality images scattering of light, color distortion and poor lighting conditions are the few characteristics that effects the natural scene of the captured image. A novel technique for extracting the foreground region from a low quality underwater image is presented in this paper. We have decomposed the image in to multiple levels based on discrete wavelet transforms (DWT) for improving the sharpness or to reduce the fogginess in the image in order to get the clear image. Subsequently, to determine the sharpness of the local patches in the image a block based SSI algorithm is presented. Finally, the segmentation is performed by computing the binary gradient mask with the Sobel edge detection algorithm along with morphological operations. The proposed method is fast, extracting the accurate foreground regions and also detect the smallest particles present in the image. The results are qualitatively compared with the improved fuzzy c-means clustering (FCM), Otsu’s Threshold and FCM thresholding by considering the static background images.

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