Mathematical models of avascular cancer

This review will outline a number of illustrative mathematical models describing the growth of avascular tumours. The aim of the review is to provide a relatively comprehensive list of existing models in this area and discuss several representative models in greater detail. In the latter part of the review, some possible future avenues of mathematical modelling of avascular tumour development are outlined together with a list of key questions

Mathematical models of avascular cancer

This review will outline a number of illustrative mathematical models describing the growth of avascular tumours. The aim of the review is to provide a relatively comprehensive list of existing models in this area and discuss several representative models in greater detail. In the latter part of the review, some possible future avenues of mathematical modelling of avascular tumour development are outlined together with a list of key questions

A Survey on Brain Tumor Classification & Detection Techniques

A cancerous or non-cancerous mass or growth of abnormal cells in the brain. The research shows that in developed countries the main cause of death of people having brain tumor is incorrect detection of brain tumor. The X-ray, CT, MRI is used for initial diagnostic of the cancer. Today Magnetic Resonance Imaging (MRI) is widely used technique for the detection of brain tumor because it provides the more details then CT. The classification of tumor as a cancerous (malignant) or non cancerous (benign) is very difficult task due to the complexity of brain tissue. In this paper, review of various techniques of classification and detection of brain tumor with the use of Magnetic Resonance Image (MRI) is discussed

Design Simulation and Assessment of Cellular Automata Based Improved Image Segmentation System

A variety of methods may be found in the numerous image segmentation techniques. Here a method of text retrieval conducted is typically to produce a collection of localized features. In computer science, object recognition is the problem of automatically "identifying", or classifying, an object. In certain instances, the awareness of artifacts is deeper into image in image segmentation through image processing. The algorithm used for image segmentation has a direct impact on the outcome of the whole approach, therefore it is important to choose carefully. It is important to choose a segmentation method appropriate for a certain framework. There are several ready-to-use segmentation methods, so one by one evaluate the tools to see which works best. Segmentation algorithms have reached such a level of complexity that a research employing them is often considered impractical. The given research undertakes the process of improved graph cut method to select the foreground and background of image through labelling and segmentation of the image. Results have been compared on the performance parameter to analyse the effectiveness of the proposed algorithm for segmentation of the images

A Novel Two-Stage Approach For Automatic Detection of Brain Tumor

Brain tumor is one of the most life-threatening diseases, and it is the most common type of cancer that occurs among those in the age group belonging to 0-19. It is also a major cause of cancer-related deaths in children (males and females) under age 20 hence its detection should be fast and accurate. Manual detection of brain tumors using MRI scan images is effective but time-consuming. Many automation techniques and algorithms for detection of brain tumors are being proposed recently. In this paper, we propose an integrated two-step approach combining modified K-means clustering algorithm and Hierarchical Centroid Shape Descriptor (HCSD). The images are clustered using modified K-means based on pixel intensity, and then HCSD helps to select those having a specific shape thus making this approach more effective and reliable. Simulation of the proposed work is done in MATLAB R2013a. Tests are carried out on T1 weighted MRI scan images

DATA SECURITY USING 2D CELLULAR AUTOMATA RULES

This paper deals with the secure transformation of text. Encryption is the most common method of hiding text from unauthorized access. Two popular ways of sending personal information in a secret way are Cryptography and Steganography. To hide the existence of the message as well as distorts the message itself using this method. LSB and ELSB are the advanced techniques used to hide text in the image. There are two levels to hide the secret information. In the first level of hiding text, data sent to images by using the password and in second level encryption of 2D Cellular Automata used to enhance more security. If one level of security technique is broken by third person then there will be one more level to provide security to that secret information. Encryption will do using 2 dimensional rules of Cellular Automata. The use of Cellular Automata rules is for the parallelism which provides high security during storing and communicating, higher compression ratio and higher encoding of data while comparing with the available security techniques