Segmentation of molecular images is a difficult task due to the low signal-to-noise ratio of images. A novel two-dimensional fuzzy C-means (2DFCM) algorithm is proposed for the molecular image segmentation. The 2DFCM algorithm is composed of three stages. The first stage is the noise suppression by utilizing a method combining a Gaussian noise filter and anisotropic diffusion techniques. The se...

High spatial resolution images have been increasingly used for urban land use/cover classification, but the high spectral variation within the same land cover, the spectral confusion among different land covers, and the shadow problem often lead to poor classification performance based on the traditional per-pixel spectral-based classification methods. This paper explores approaches to improve ...

An image analysis system (IAS) was developed for the quantitative assessment of estrogen receptor’s (ER) positive status from breast tissue microscopy images. Twenty-four cases of breast cancer biopsies, immunohistochemically (IHC) stained for ER, were microscopically assessed by a histopathologist, following a clinical routine scoring protocol. Digitized microscopy views of the specimens were ...

In this paper an automatic classification algorithm is proposed for the diagnosis of the liver steatosis, also known as, fatty liver, from ultrasound images. The features, automatically extracted from the ultrasound images used by the classifier, are basically the ones used by the physicians in the diagnosis of the disease based on visual inspection of the ultrasound images. The main novelty of...

A novel statistical textural distinctiveness approach for robustly detecting salient regions in natural images is proposed. Rotational-invariant neighborhood-based textural representations are extracted and used to learn a set of representative texture atoms for defining a sparse texture model for the image. Based on the learnt sparse texture model, a weighted graphical model is constructed to ...

The research work deals with an approach to perform texture and morphological based retrieval on a corpus of food grain images. The work has been carried out using Image Warping and Image analysis approach. The method has been employed to normalize food grain images and hence eliminating the effects of orientation using image warping technique with proper scaling. The images have been properly ...

This paper presents a way to classify between the nest image and the non-nest image. The input image is processed by Canny Edge Detection, and using Local Binary Pattern to extract the local textural features. K-means was applied to clustering LBP feature. And, dividing images into several blocks and finding out the dominant local textural features for describing the corresponding block. The pe...

This paper presents two-stage unsupervised terrain classification of fully polarimetric SAR data using Freeman and Durden decomposition based on three simple scattering mechanisms: surface, volume and double bounce (first step), and textural features (uncorrelated uniformity, contrast, inverse moment and entropy) obtained from grey level co-occurrence matrices (GLCM) (second step). Textural fea...

In this paper, we present a method for extraction and attribute selection for textural features classification using the fusion of information from the mediolateral oblique (MLO) view and craniocaudal (CC) views. In the extraction step, wavelet coefficients together with singular value decomposition technique were applied to reduce the number of textural attributes. For the selection stage and ...

Striped stem-borer (SSB) infestation is one of the most serious sources of damage to rice growth. A rapid and non-destructive method of early SSB detection is essential for rice-growth protection. In this study, hyperspectral imaging combined with chemometrics was used to detect early SSB infestation in rice and identify the degree of infestation (DI). Visible/near-infrared hyperspectral images...