2012 International Conference on Biomedical Engineering (ICoBE),Penang,Malaysia,27-28 February 2012
Bone Marrow Image Segmentation Based on Multilevel Thresholding R. Adollah
M.Y Mashor
School of Mechatronic Universiti Malaysia Perlis Kangar, Perlis
[email protected]
Institute for Postgraduate Studies University Malaysia Perlis Kangar, Perlis
[email protected]
E. U. Francis School of Mechatronic Universiti Malaysia Perlis Kangar, Perlis
[email protected]
Abstract—this paper represents an approach of image segmentation on microscopic bone marrow images by using multilevel tresholding technique. This technique has been applied into two types of images which is normal bone marrow and Acute Lymphoblastic Leukemia (ALL). Generally, image processing technique involved five basic components which are image acquisition, image pre-processing, image segmentation, postprocessing and image analysis. The most critical step in image processing is segmentation of the image. A multilevel tresholding are proposed as a method in segmentation of WBC from its complicated background. This segmentation technique expected to be one of segmentation technique that could possibly to differentiate well between normal bone marrow and ALL in further analysis. Keywords; image segmentation; multilevel tresholding; image histogram; bone marrow image;microscopic images
I.
INTRODUCTION
Nowadays, several research groups have focused on the development of computerized systems that capable to analyze different types of medical images and extract useful information for the medical professional. The major objectives of computer aided analysis in medical field are to gather the information, screening or investigating, diagnose, therapy and control, monitoring and evaluation. In other word, the main purpose of biomedical imaging and image analysis is to provide a certain benefit to the subject or patient [1-2]. Diagnosing ALL begins with a medical history, physical examination, complete blood count and bone marrow biopsy. A biopsy is the only sure way to know whether leukemia cells are in bone marrow. Leukemia causes a very high level of white blood cells. It may also cause low levels of platelets and hemoglobin. Typically, the higher the white blood cell count,
978-4577-1991-2/12/$26.00 ©2011 IEEE
N. H. Harun School of Mechatronic University Malaysia Perlis Kangar, Perlis
[email protected]
the worse the prognosis. Blast cells are seen on blood smear in majority of cases [3-4]. Particularly, the identification and differential count of blood’s cell is a time-consuming and repetitive task that can be influenced by operator’s accuracy and tiredness [5]. Consequently, to overcome the tedious and time-consuming task of human experts, many automated techniques have been proposed [5-6]. In order to automate the bone marrow analysis, segmentation is the most important step and we proposed a simple segmentation method by using multilevel thersholding. II.
BONE MARROW IMAGES
In this paper, two types of bone marrow images are used, which is normal and abnormal bone marrow with Acute Lymphoblastic Leukemia (ALL) type as illustrates in Figure 1. Leukemia is cancer of the blood and develops in the bone marrow. The bone marrow is the soft, spongy center of the long bones that produces the three major blood cells which are white blood cells, red blood cells and platelets [5].
Normal bone marrow
Acute Lymphoblastic Leukemia (ALL)
Figure 1. Images of Bone Marrow With 40 x magnifications (ALL)
III.
SEGMENTATION
Segmentation refers to the process of partitioning a digital image into multiple regions. The goal of segmentation is to simplify or to change the representation of an image into something that is more meaningful and easier to analyze [7]. Some of the practical application of image segmentation is used in medical imaging to study of anatomical structure, diagnosis and other pathologies. The purpose of segmentation is that objects and background are separated into non-overlapping sets and only remains the object of interest. Nowadays, numerous algorithms and techniques have been developed for image segmentation [3, 4, 7]. A. Multilevel Tresholding Segmentation Thresholding methods can be categorized into two groups as Global Thresholding and Local Thresholding [8]. Global thresholding is a simple and efficient method where a defined or computed threshold value is used to separate foreground objects from background. In Local Thresholding this method is by assigning of a value to each pixel to determine whether it is a foreground or background pixel using local information from the image. Thresholding is an important technique for image segmentation that tries to identify and extract a target from its background on the basis of the distribution of gray levels or texture in image objects. Most thresholding techniques are based on the statistics of the one-dimensional histogram of gray levels and on the two-dimensional cooccurrence matrix of an image [9]. In thresholding-based segmentation, the image histogram can be partitioned into two classes using a single value (called bi-level thresholding) or multiple classes using multiple values (called multi-level thresholding) based on the characteristics of the histogram [10]. In the bi-level tresholding, pixels with intensity values less than the threshold are set as background while others are set as object. In multiple-level thresholding, pixels with the intensity values between two successive thresholds are assigned as a class. Theoretically, the levels of thresholding can be increased infinitely according to the number of objects present in images, but the computation load will also be increased exponentially. Concept of multilevel thresholding divides the histogram into several groups. The pixels, having gray levels within a specific range defined by adjacent two thresholds, are classified into a corresponding group. A great number of multilevel thresholding methods have been proposed for segmentation [11]. B. Steps of prochedures There are 5 steps involved in segmentation based on multilevel tresholding by considering image histogram as described below:
i.
The first step is image capturing of bone marrow slide under 40 x magnifications via Infinity 2 digital camera mounted on Leica microscope ii. Then, save the images under .bmp extension. iii. The RGB images then converted into gray level images iv. Images histogram is used to find two value of threshold, T1 and T2, as applying multilevel tresholding to the image. v. Segmented of object interest. An image may be defined as a two-dimensional function, f(x, y), where x and y are spatial coordinates, and the amplitude of f at any pair of coordinates (x, y) is called the intensity or gray level of the image at that point. Multilevel thresholding classifies a point (x, y) as belonging to one object class if T1 < (x, y) T2 and to the background if f(x, y)
