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\title{Automatic Classification and Segmentation of Tumors from Skull Stripped Images using PNN \& SFCM}
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\begin{document}

             \author[1]{Adapala Praveen  Kumar}

             \author[2]{J Krishna  Chaithanya}

             \affil[1]{  JNTUH}

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\date{\small \em Received: 7 December 2014 Accepted: 2 January 2015 Published: 15 January 2015}

\maketitle


\begin{abstract}
        


Automatic classification of brain tumor is area of concern from last few decades for better perceptive analysis in accurate manner. In this paper an automatic brain tumor classification approach namely probabilistic neural network are proposed with image and data processing techniques. The conventional algorithms which are reported in the literature are not automatic in nature and mainly their processing is based on human inspection. Then after some time a new classification approaches came into existence by overcoming the disadvantages of conventional algorithms namely Operator assisted classification methods which proves impractical for huge data amounts and simultaneously it is non-reproducible. The MR brain tumor images contains the noise like content which is mainly caused by the operator performance while processing and this noise results in highly inaccurate classification analysis. For better accuracy in classification of tumor image artificial intelligent techniques like fuzzy logic and neural networks usage are encouraged these days.

\end{abstract}


\keywords{probabilistic neural network (PNN), principle component analysis, SFCM, brain tumor.}

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\let\tabcellsep& 	 	 		 
\section[{Introduction}]{Introduction}\par
esearch on brain and its related tumors are area of concern from last few decades in the medical image processing and data processing techniques. In literature so many different classification algorithms are reported for differentiating the area of tumor region from the respective brain region. Although so many innovative classification techniques are implemented for accurately analyze the situation of patient but due to high complexity and noise related issues this conventional techniques are not considered as promising tool for classification of tumor because this conventional algorithms are not designed meet the practical scenario requirements and produces inaccuracy results.\par
The drawbacks and disadvantages of conventional classification algorithms gives path to the new way to research on brain tumor and researchers starts to make research on artificial independent techniques like fuzzy logic and neural networks which are considered as promising technologies in many application oriented domains. Automatic image classification of brain tumor is motivated by these artificial independent algorithms in medical image processing domain. As the research relates to human life so the classification results should give less error rate and high accuracy for better analysis and that's why computer assistance is demanded these days for automatic classification.\par
Advantages of the proposed method stress not only on the classification but also concentrates to extract the abnormal image region using clustering based segmentation algorithm.\par
Double reading of tumor images can give better accuracy but this could also increase the cost so to tackle the expensiveness of double reading a better software is needed and is in great interest now a days. The main difference between the conventional and artificial intelligent techniques is conventional techniques resolve the issue of handling the large number of patients in accurate manner.\par
In the proposed work a new automatic approach is presented for classification of brain MR images automatically by make use of some extensive like pixel intensity from the tumor brain image and some anatomical features. The automatic and most reliable methods for detection of tumor is an area of interest and in great need for analyzing the patient scenario by respective physician and these automatic techniques are in great demand mainly because of drawbacks of conventional approaches.\par
The proposed PNN approach is not yet used to its full extent in the classification of data from the respective brain images which gives the data related to brain tumor and if the Proposed PNN is utilized manually then problems related to handling high amount of data of different patients at intensive care units can be 
\section[{Global Journal of C omp uter S cience and T echnology}]{Global Journal of C omp uter S cience and T echnology}\par
Volume XV Issue I Version I Year ( ) F handled very efficiently and simultaneously reduces the time consumption significantly. The PNN approach can be efficiently used for both clustering, recognition and classification for better accuracy and it gives scope to introduce the neural networks systems in order to solve the medical problems. 
\section[{II.}]{II.} 
\section[{Tumor Image Compression by Principle Component Analysis}]{Tumor Image Compression by Principle Component Analysis}\par
Many researchers considered principle component analysis is most promising tool for feature extraction and the main purpose of principle component analysis is reduce the size of large data in terms of dimensionality. The MR brain recognition system is designed to recognize the test image according to the memory. The respective memory which is used to recognize the test here here here The conversion of respective image into pixel vector ? 1 is essential to extract necessary features of ? 1 by using the PCA analysis technique and the dimensionality of the vector ? 1 will be M×N. In the proposed work the PCA approach is used as reducing the dimensionality reduction technique further it transforms the vector ? 1 to a vector ð??"ð??" 1 which has the dimensionality d where d<< M×N . For each training image ? 1 these feature vectors ð??"ð??" 1 are calculated and stored. In the testing phase feature vector of the test image is computed and checks the similarity between the feature vectors using the Euclidian distance approach. The identity of the most similar is treated as recognizer output.  {\ref IMAGE} 1IMAGE 2 IMAGE 3 IMAGE 4 Feature extractor [X 1 , X 2 , X 3 ? X n ] Feature extractor [X 1 , X 2 , X 3 ? X n ] Feature extractor [X 1 , X 2 , X 3 ? X n ] Feature extractor [X 1 , X 2 , X 3 ? X n ]\par
Feature vector of image  
\section[{Probabilistic Neural Networks}]{Probabilistic Neural Networks}\par
The probabilistic neural networks are also termed as artificial independent algorithm which is developed by Donald Specht and most researchers termed the PNN as ideal solution to the pattern classification related problems and the specific classification is termed as Bayesian classifiers. PNN is simple in structure and its training speed is many times faster than the respective BP network and it is robust to noise too. The most important advantage of PNN is its training is easy and the respective weights are not trained but assigned. The structure of PNN is s follows a) Input layer\par
The input vector, denoted as p, is presented as the black vertical bar in Fig.  {\ref 2}. Its dimension is R × 1. In this paper, R = 3. 
\section[{b) Radial Basis Layer}]{b) Radial Basis Layer}\par
The vector distances between input vector p and the weight vector made of each row of weight matrix W are calculated. Here, the vector distance is de fined as the dot product between two vectors \hyperref[b7]{[8]}. Assume the dimension of W is Q×R. The dot product between p and the i-th row of W produces the i-th element of the distance vector ||W?p||, whose dimension is Q×1, as shown in Fig.  {\ref 2} where Wi is the vector made of the i-th row of W and bi is the i-th element of bias vector b. 
\section[{c) Some characteristics of Radial Basis Layer}]{c) Some characteristics of Radial Basis Layer}\par
The i-th element of a equals to 1 if the input p is identical to the ith row of input weight matrix W. A radial basis neuron with a weight vector close to the input vector p produces a value near 1 and then its output weights in the competitive layer will pass their values to the competitive function. It is also possible that several elements of a are close to \hyperref[b0]{1}  
\section[{since the input pattern is close to several training patterns d) Competitive Layer}]{since the input pattern is close to several training patterns d) Competitive Layer}\par
There is no bias in Competitive Layer. In Competitive Layer, the vector a is firstly multiplied with layer weight matrix M, producing an output vector d. The competitive function, denoted as C in Fig.  {\ref 2}, produces a 1 corresponding to the largest element of d, and 0's elsewhere. The output vector of competitive function is denoted as c. The index of 1 in c is the number of tumor that the system can classify. The dimension of output vector, K, is 5 in this paper. 
\section[{IV. Spatial Fuzzy C-Means Clustering}]{IV. Spatial Fuzzy C-Means Clustering}\par
A conventional FCM algorithm does not fully utilize the spatial information in the image. In this paper, we present a fuzzy c-means (FCM) algorithm that incorporates spatial information into the membership function for clustering. The spatial function is the summation of the membership function in the neighborhood of each pixel under consideration. The advantages of the new method are the following: (1) it yields regions more homogeneous than those of other methods, (2) it reduces the spurious blobs, (3) it removes noisy spots, and (4) it is less sensitive to noise than other techniques. This technique is a powerful method for noisy image segmentation and works for both single and multiple-feature data with spatial information.\par
The following are the steps involved in SFCM based clustering algorithm  Then, the PNN is used to classify the MR images. Lastly, performance based on the result will be analyzed at the end of the development phase. The proposed brain MR images classification method is shown in Fig.  {\ref 3}.\par
In addition to the brain classification a new spatial fuzzy C-means clustering approach is implemented to extract the abnormal regions of the given image is abnormal. The man advantage of using SFCM in this approach is extract the very keen regions from the abnormal region fast \& accurately than the conventional FCM (Fuzzy C-means) algorithm. The proposed approach proves to be more effective for the given set of images.\par
V.   
\section[{Experimental Results}]{Experimental Results} 
\section[{Conclusion}]{Conclusion}\par
In the area of medical image processing, classification and analysis of MR brain image is the challenging task. The proposed PROBABILISTIC NEURAL NETWORKS is unique in terms of simple structure and performs fast on training with high speed when compare to conventional classification approaches. In this paper 23 different types of MR brain images are taken into consideration for training and testing to yield the better accuracy in terms of analysis and the respective the testing were conducted on different set of images. The reported PROBABILISTIC NEURAL NETWORKS classifier is examined under different spread values and these spread values also considered as the smoothing factor. In the experimental simulation results the accuracy of proposed classifier varies from 100\% to 70\% which depends on the taken spread values. In addition to the brain classification a new spatial fuzzy C-means clustering approach is implemented to extract the abnormal regions of the given image is abnormal.\begin{figure}[htbp]
\noindent\textbf{1}\includegraphics[]{image-2.png}
\caption{\label{fig_0}Figure 1 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-3.png}
\caption{\label{fig_1}Feature}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-4.png}
\caption{\label{fig_2}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-5.png}
\caption{\label{fig_3}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{23}\includegraphics[]{image-6.png}
\caption{\label{fig_4}Figure 2 :Figure 3 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{432}\includegraphics[]{image-7.png}
\caption{\label{fig_5}Figure 4 : 3 Figure 2}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{1} \par 
\begin{longtable}{P{0.46363636363636357\textwidth}P{0.38636363636363635\textwidth}}
SPREAD VALUE\tabcellsep ACCURACY\\
1\tabcellsep 72\\
2\tabcellsep 78\\
3\tabcellsep 100\\
VI.\tabcellsep \end{longtable} \par
 
\caption{\label{tab_0}Table 1 :}\end{figure}
 			\footnote{© 2015 Global Journals Inc. (US)} 			\footnote{© 2015 Global Journals Inc. (US) 1} 		 		\backmatter  			  				\begin{bibitemlist}{1}
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