Statistical geometric features-extensions for cytological texture analysis
نویسندگان
چکیده
Statistical Geometric Features (SGF) have recently been proposed for the classification of image textures. The SGF method is easily extended to use other geometric properties of connected regions. Following a brief review of the method, we propose such an extension to the set of SGF features for the purpose of classifying cervical cell textures. The resulting method proves to be as powerful as the Gray Level Co-occurrence Matrix (GLCM) method of texture analysis, when tested on a set of 117 cervical cell images. The ability to define features tailored to the geometric properties of the textures concerned makes this method a powerful analysis tool.
منابع مشابه
Texture Analysis
This chapter reviews and discusses various aspects of texture analysis. The concentration is on the various methods of extracting textural features from images. The geometric, random field, fractal, and signal processing models of texture are presented. The major classes of texture processing problems such as segmentation, classification, and shape from texture are discussed. The possible appli...
متن کاملComputerize classification of Benign and malignant thyroid nodules by ultrasound imaging
Introduction: Early detection and treatment of thyroid nodules increase the cure rate and provide optimal treatment. Ultrasound is the chosen imaging technique for assessment of thyroid nodules. Confirmation of the diagnosis usually demands repeated fine needle aspiration biopsy (FNAB). So, current management, has morbidity and non zero mortality. The goal of the present study ...
متن کاملStatistical Analysis of Features and Classifiers in Identifying Nodules and Its T Staging in Lung Ct Images
Lung cancer is the most common disease with greater morality rate. Computed Tomography (CT) images are used for early diagnosis of lung cancer with the help of CAD system. Selection of effective feature set and proper classifier for medical images where machine learning techniques are used is a challenging task. Texture analysis of computed tomography (CT) images is one of the important prelimi...
متن کاملInvariant texture analysis through Local Binary Patterns
In many image processing applications, such as segmentation and classification, the selection of robust features descriptors is crucial to improve the discrimination capabilities in real world scenarios. In particular, it is well known that image textures constitute power visual cues for feature extraction and classification. In the past few years the local binary pattern (LBP) approach, a text...
متن کاملOnline Streaming Feature Selection Using Geometric Series of the Adjacency Matrix of Features
Feature Selection (FS) is an important pre-processing step in machine learning and data mining. All the traditional feature selection methods assume that the entire feature space is available from the beginning. However, online streaming features (OSF) are an integral part of many real-world applications. In OSF, the number of training examples is fixed while the number of features grows with t...
متن کامل