H.SH.Rostom Utilization of improved masks for edge detection images

Transcription

1 75 H.SH.Rostom Utilization of improved masks for edge detection images Abstract In the present paper, algorithm is proposed to create a connected boundaries components using the local features minutiae points in image as objects image called (A. H.SH.Rostom) algorithm The Group of (A. H.SH.Rostom) algorithm consists of 10 masks were geometry of the mask operator determines a characteristic direction in which it is most sensitive to edges applied to the four stages of the Mycosis Fungoides disease Skin image have been identified and the edges of the images used for each and every stages that the database consists of 40 images divided each stage of the Mycosis Fungoides disease Skin image 10 images. The experimental results confirm the effectiveness of the proposed A. H.SH.Rostom Utilization of improved masks for Image Edge Detecting of Mycosis Fungoides. Keywords: Segmentation, Image processing, Skin image detection, Mycosis Fungoides. المستخلص: تم اقتراح خوارزمية إلنشاء مكونات حدود المكونات باستخدام خصائص مميزة للتفاصيل المحلية في الصورة سميت الخوارزمية باسم A.H.SH.Rostom تتكون الخوارزمية من عشرة اقنعة تحدد االلية المستخدمة لتحديد االتجاه بحيث يكون االكثر دقة. استخدمت لتحديد حواف مرض Mycosis Fungoides علما ان قاعدة البيانات تتكون من 04 صورة عشرة صور في كل مرحله. اثبتت النتائج التجريبية فعالية الخوارزمية وامكانية تحديد حواف مرض Mycosis Fungoides

2 75 1- INTRODUCTION Image segmentation is an important step in image analysis, pattern recognition, and computer vision. In radar images, for oil slicks detection, the segmentation is the main step for detecting the oil slick and defining its boundary. Detection of edge is a critical element in image processing, since edges contain a major function of image information [1]. Edge detection of an image reduces significantly the amount of data and filters out information that may be regarded as less relevant, preserving the important structural properties of an image. Therefore, edges detected from its original image contain major information, which only needs a small amount of memory to store[2]. Examples of approaches to edge detection include algorithms such as the Sobel, Prewitt and Roberts edge detectors which are based on the _rst order derivative of the pixel intensities. The Laplacian-of-Gaussian (LoG) edge detector is another popular technique, using instead the second order di_erential operators to detect the location of edges [3]. However, all of these algorithms tend to be sensitive to noise, which is an intrinsically high frequency phenomenon. To solve this problem the Canny edge detector was proposed, which combines a smoothing function with zero crossing based edge detection [4]. In real world applications, medical images contain object boundaries and object shadows and noise. Therefore, they may be difficult to distinguish the exact edge from noise or trivial geometric features [6]. Edges are effected by noise present in an image though.an edge may be regarded as boundary between two dissimilar regions in an image.edge detection is a terminology in image processing and computer vision, particularly in areas of feature detection and feature extraction[7]. The paper is organized as follows; Section 2 deals with the A. H.SH.Rostom Utilization of improved masks are considered to

3 75 determine the Mycosis Fungoides Skin image area. Section 3 deals with the Edge Operations opening to perform the connected component analysis, section 4 gives the overview of algorithm with Experimental Results and last section 5 ends the paper with conclusion. 2. A. H.SH.Rostom Utilization of improved masks A human skin color model is used to decide either a pixel is skin color or non skin-color[5]. In this research, we use mew method called(a. H.SH.Rostom Utilization of improved masks ) Mycosis Fungoides Skin image edge detection. A. H.SH.Rostom Utilization of improved masks consists of 10 masks determines a characteristic direction edge. The Details of values for each mask in A. H.SH.Rostom Utilization of improved masks shown in figure(1) and the skin images library samples, two types of images (A)Samples with Mycosis Fungoides diseases Skin images (B) Samples with other diseases shown in figure(2).

4 61 A. H. SH.Rostom Group masks determined for direction edge: M1= Vertical1,M2=Horizontal1 M3= Vertical-Horizontal1 M4= Vertical2 M5= Horizontal2 M6= Vertical-Horizontal2 M7=Diagonal1, M8= Diagonal2 M9= Diagonal1- Diagonal2 M10=Mask roots Figure(2): The skin library samples, (A) Samples with Skin disease (B) Samples with other diseases 4. Experimental Results In this section a detailed experimental comparison of the above stated A. H.SH.Rostom Utilization of improved masks has been presented. We have used two types Mycosis Fungoides Skin image databases:

5 61 (1) database prepared in our conditions,images obtained from in Al-Sder Hospital. (2) Skin database [4] and some other images obtained from internet. Experimental Results for Appling Edge Detection mask shown in figure (3,4,5,6,7,8,9,10,11,12). 7. Conclusion In this paper we showed how edge detection can be used to preserve Mycosis Fungoides images. The subjective test algorithm that the proposed method outperforms most of the other edge detection methods. Thus, our method is of special interest for medical images systems which need to show sensitive local edge detection information while preserving the context of actions. Figure(3):(A):Original Skin Image, (B) Edge detection by M1 for A for all Mycosis Fungoides Skin

6 64 Figure(4):(A):Original Skin Image, (B) Edge detection by M2 for A for all Mycosis Fungoides Skin Figure(5):(A):Original Skin Image,

7 66 (B) Edge detection by M3 for A for all Mycosis Fungoides Skin Figure(6):(A):Original Skin Image, (B) Edge detection by M4 for A for all Mycosis Fungoides Skin Figure(7):(A):Original Skin Image,

8 62 (B) Edge detection by M5 for A for all Mycosis Fungoides Skin Figure(8):(A):Original Skin Image, (B) Edge detection by M6 for A for all Mycosis Fungoides Skin Figure(9):(A):Original Skin Image, (B) Edge detection by M7 for A for all Mycosis Fungoides Skin

9 67 Figure(10):(A):Original Skin Image, (B) Edge detection by M8 for A for all Mycosis Fungoides Skin Figure(11):(A):Original Skin Image,

10 66 (B) Edge detection by M9 for A for all Mycosis Fungoides Skin Figure(12):(A):Original Skin Image, (B) Edge detection by M10 for A for all Mycosis Fungoides Skin Table (1) shown the comparison for A. H.SH.Rostom Utilization of improved masks for Mycosis Fungoides Skin image Edge detection. Edge detection are computationally more expensive compared to M1,M2,M4,M5,M7 and M8 masks. However, the M10 edge detection algorithm performs better than all these operators under almost all scenarios. Evaluation of the images showed that under noisy conditions, M10,M9,M6 and M3 exhibit better performance, respectively.

11 65 References [1] Ali El-Zaart, Fingerprint Images Segmentation, Journal of Computer Science 6 (2): , [2] Michael Padilla and Zihong Fan, EE368 Digital Image Processing Project Automatic Face Detection Using Color Based

12 65 Segmentation and Template/Energy Thresholding, EE368 - Dr. B. Girod, Stanford University, Spring [3] W. Dai and K. Wang, \An image edge detection algorithm based on local entropy," in Proceedings of theieee International Conference on Integration Technology (ICIT'07,2007. [4] J. F. Canny, \Computational approach to edge detection," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 8, pp. 679{698, [5] Balkrishan Ahirwal, Mahesh Khadtare and Rakesh Mehta, FPGA based system for Color Space Transformation RGB to YIQ and YCbCr. International Conference on Intelligent and Advanced Systems,2007. [6] Huertas, A. and Medioni, G., Detection of intensity changes with sub pixel accuracy using Laplacian-Gaussian masks, IEEE Trans. On Pattern Analysis and Machine Intelligence, PAMI, vol. 8, pp , [7] Beant Kaur, Anil Garg, Amandeep Kaur, Mathematical Morphological Edge Detection For Remote Sensing Images, IJECT Vol. 1, Issue 1, December 2010.