Morphological Tumor Markers ab 138.99 € als Taschenbuch: General Aspects and Diagnostic Relevance. Softcover reprint of the original 1st ed. 1987. Aus dem Bereich: Bücher, Wissenschaft, Medizin,
This book aims to develop an intelligent breast cancer identification (ICBIS) system based on image processing techniques and neural network classifier. Recently, many researchers have developed image recognition systems for classifying breast cancer tumors using different image processing and classification techniques. The challenge is the extraction of the real features that distinguish the benign and malignant tumor. The classifications of breast cancer images have been performed using the shape and texture characteristics of the images. The asymmetry, roundness, intensity levels and more are the exact shape and texture features that distinguish the two types of breast tumors. Image processing techniques are used in order to detect tumor and extract the region of interest from the mammogram. The following data processing operations have been done for detection of images: thresholding, filtering and adjustments, canny edge detection, and some morphological operations. Shape and texture features are then extracted using GLCM (Gray-Level Co-Occurrence Matrix) algorithm in order to accurately classify the mammograms into normal, benign, and malignant tumors.
Over the last few years segmentation of brain tissues and abnormalities has been an important area in medical imaging. Several morphological imaging techniques have been used to solve this problem. Many fractal based features have been used to analyze brain tumors. These fractal based methods have improved segmentation of brain tumor. This book provides features selection techniques based on statistical model for selecting best features from subset of features and use it for segmentation. Two statistical methods based on Kullback Leibler Divergence and Bayesian models have been analyzed along with segmentation techniques such as SOM and EM. The analysis should help professionals who are working for computer analysis diagnosis of brain tumors.
The main objective of this thesis is to detection of brain tumor with the results provided by image processing of the patient's MRI image.The brain tumor is a highly aggressive disease, which is difficult to resects totally. The surgical extent of resection constitutes a key role due to its direct influence on the patient's survival time. For detecting grayscale accurately Magnetic resonance imaging (MRI) is a common approach. the key objective of this thesis is to form a methodology to detect & extraction of brain tumor from patient's MRI scan images of the brain. This method incorporates with some noise removal functions, segmentation and morphological operations which are the basic concepts of image processing.Our proposed method will take input from MRI image. Input image will convert to gray scale image, then it will be adjusted based on the maximum intensity level, for avoiding excess data.
Aloe vera is one of the few medicinal plants used in home remedies since ancient times. The dried sap of the Aloe plant is traditionally used for diabetes. Some of the most important other pharmacological activities of Aloe vera are antiseptic, anti- tumor, anti-inflammatory and wound and burn healing effect In order to fulfill the demand for aloe, a large number of authentic planting materials are required for cultivation throughout the year and thus, micropropagation would be an attractive method as an alternative for the conventional propagation of Aloe vera. Apart from Aloe vera, other economically important species of Aloe include A. ferox Mill, A. Africana Mill, A. perryi Back. and A. arborescence. Therefore, it is also required to systematically characterize this economically important genus at molecular level in relation to morphological variation for estimation of genetic diversity. Hence, it is hoped that present compilation would be of great use to the Aloe vera researchers, teachers and students. It may also prove book a reference as to the agencies engaged in natural product formation from Aloe vera.
The proposed brain tumor detection and localization framework comprises following steps: (i) Image acquisition, (ii) Preprocessing, (iii) Edge detection, (iv) Morphological operations. After thresholding operations, tumors appear as pure white color on pure black backgrounds. The algorithm has two stages, first is pre-processing of given MRI image and after that segmentation is done. - Image acquisition (gathering of MRI scanned images). - Images stored in MATLAB in form of 2-D matrix. - Preprocessing is done (i) Image acquisition (gathering of MRI scanned images). (ii) Edge detection through median filtering. (iii) Image dilation - Processing stage It includes analysis on 2-D image through MATLAB commands,extraction of tumor portion is done with the help of segmentation methods.
Ganoderma lucidum (Leyss. ex Fr.) Karst. (Ling Zhi) (Aphyllophorales) (the family Polyporaceae) was first indexed in the Shen Nong s Materia Medica (206 BC 8 AD) as a longevity-promoting and tonic herb of the non-toxic superior class, and has been used in traditional Chinese medicine (TCM) for more than 2000 years to prevent and/or treat various human diseases such as hepatitis, chronic bronchitis, gastritis, tumor growth and immunological disorders. According to Fuzheng Guben , one of the major TCM therapeutic principles, Ganoderma lucidum (Gl) is capable of strengthening body resistance and improving constitutive homeostasis in patients (Lin, 2001). The name Ganoderma is derived from the Greek ganos/ "brightness, sheen", hence "shining" and derma/ "skin", while the specific epithet lucidum in Latin for "shining" and tsugae refers to being of the Hemlock (Tsuga). Another Japanese name is mannentake, meaning "10 000 year mushroom" (Liddell et al., 1980). The genus Ganoderma was named by Karsten in 1881. Members of the family Ganodermataceae were traditionally considered difficult to classify because of the lack of reliable morphological characteristics.
The capacity to reliably track, model andcharacterize morphometric changes in anatomicstructures and tumors from 3-D images sequences isextremely valuable in staging disease progression andassessing response to treatment. This book providesthe design and evaluation of two approaches to facilitate clinical assessment in diagnosticradiology. The first is a tool for performingcomparative morphological analysis of ventricles fromMR brain scans of patients with Bipolar Disorder or Asperger''s Syndrome. Ventricles characterizationusing low frequency elliptic Fourier descriptorsprovides an accurate representation while allowingfor reliable group separation. The second is a finiteelement model (FEM) deformable registration techniqueof pre- and post-treatment CT images, to track and quantify tumor response to radiofrequency ablation ofpatients with liver malignancies. Advanced clinicalapplications have become a critical component of thework flow of radiologists as well as the team ofother clinicians. These models should be especiallyuseful for future algorithm development that directly meet the requirements for a range of interventionaland diagnostic procedures.