Abstract: Tumor is an uncontrolled growth of tissues in any part
of the body. Tumors are of different types and they have different
characteristics and treatments. Brain tumor is inherently serious and
life-threatening because of its character in the limited space of the
intracranial cavity (space formed inside the skull). Locating the tumor
within MR (magnetic resonance) image of brain is integral part of the
treatment of brain tumor. This segmentation task requires
classification of each voxel as either tumor or non-tumor, based on
the description of the voxel under consideration. Many studies are
going on in the medical field using Markov Random Fields (MRF) in
segmentation of MR images. Even though the segmentation process
is better, computing the probability and estimation of parameters is
difficult. In order to overcome the aforementioned issues, Conditional
Random Field (CRF) is used in this paper for segmentation, along
with the modified artificial bee colony optimization and modified
fuzzy possibility c-means (MFPCM) algorithm. This work is mainly
focused to reduce the computational complexities, which are found in
existing methods and aimed at getting higher accuracy. The
efficiency of this work is evaluated using the parameters such as
region non-uniformity, correlation and computation time. The
experimental results are compared with the existing methods such as
MRF with improved Genetic Algorithm (GA) and MRF-Artificial
Bee Colony (MRF-ABC) algorithm.
Abstract: Many approaches to pattern recognition are founded on probability theory, and can be broadly characterized as either generative
or discriminative according to whether or not the distribution of the image features. Generative and discriminative models have
very different characteristics, as well as complementary strengths and weaknesses. In this paper, we study these models to recognize the patterns of alphabet characters (A-Z) and numbers (0-9). To handle isolated pattern, generative model as Hidden Markov Model (HMM) and discriminative models like Conditional Random Field (CRF), Hidden Conditional Random Field (HCRF) and Latent-Dynamic Conditional Random Field (LDCRF) with different number of window size are applied on extracted pattern features. The gesture recognition rate is improved initially as the window size increase, but degrades as window size increase further. Experimental results show that the LDCRF is the best in terms of results than CRF, HCRF and HMM at window size equal 4. Additionally, our results show that; an overall recognition rates are 91.52%, 95.28%, 96.94% and 98.05% for CRF,
HCRF, HMM and LDCRF respectively.