This study presents a computer algorithm, which consists of four main steps (image acquisition, image pre-processing, nodule candidate detection, and feature extraction) for nodule detection in chest radiographs. Algorithm is applied on small-cell type of lung cancer (SCLC) and non-small-cell type of lung cancer (NSCLC) images. Total 50 images (25 from each category) were used to estimate geome...

The term CBIR refers to the process of retrieving similar images from a large collection of image database. The image retrieval is done on the basis of similarity matching between query image and database images. Different feature extraction techniques are being used to extract features of an image. The important image features are Color, Texture, Shape, Spatial location, edges etc. The CBIR (C...

A robust method is proposed in this paper to retrieve digital images using combination of color, texture and shape features. The proposed method consists of three stages. Shape detection is done based on TopHat transform to detect and crop object part of the image in the first stage. The second is included a texture feature extraction algorithm using color local binary patterns (CLBP) and local...

We describe our participation in the plant identification task of ImageClef 2013. We submitted one fully automatic run that uses different features for the uniform background (isolated leaves) and natural background (unconstrained photos) categories. Besides the category information, meta-data was only used in the natural background category. Our approach employs a variety of shape, texture and...

The rapid development of technologies and steadily growing amounts of digital information highlight the need of developing an accessing system. Contentbased image indexing and retrieval has been one of the most important research areas in computer science for the last few decades. In this thesis, the approaches of contentbased image retrieval using low level features such as color, shape and te...

The classification of satellite image has various issues including image quality. The aim of classifying satellite image is to provide better understanding of image . The classification of image depends on various features. the present paper focuses on five categories of satellite image such as residential area, agriculture, desert, mountain and forest. The objective of this paper is to study t...

The maturity level of guava fruit can be determined by looking at various factors. Shape is one the factors that play a role in identifying certain objects. classification seen from shape, texture and color. shape quite diverse ranging round (Round shape) to oval (Pear shape). So Matlab application was built determine type based on its color, texture. K-Nearest Neighbor classify objects learnin...

In this paper, we use non-subsampled shearlet transform (NSST) and Krawtchouk Moment Invariants (KMI) to realize image retrieval based on texture and shape features. Shearlet is a new sparse representation tool of multidimensional function, which provides a simple and efficient mathematical framework. We decompose the images by NSST. The directional subband coefficients are modeled by Generaliz...

Color, texture, and shape act as important information for images in human recognition. For content-based image retrieval, many studies have combined color, features to improve the retrieval performance. However, there not been powerful methods combining all features. This study proposes a method that uses local global of shape. The color are extracted from autocorrelogram; texture magnitude co...

In this paper we propose a new and effective image retrieval scheme using color, texture and shape features based on pseudo-Zernike moment. Image is predefined by using fast color quantization algorithm with cluster merging. A small number of dominant colors and their percentage can be obtained. The spatial features are extracted using steerable filter decomposition. This offers an efficient an...