Computer-Assisted Approach to Anemic Erythrocyte Classification Using Blood Pathological Information

Maitreya Maity, Prabir Sarkar, Chandan Chakraborty

School of Medical Science and Technology, Indian Institute of Technology-Kharagpur Kharagpur, West Bengal, India

Abstract—Pathological blood test is one of the most important key issues in medical field prior to disease diagnosis. The aim of this paper is to design and develop a standalone application for the purpose of both acquisition and management of patient blood pathological information and generate automated anemia diagnosis report using computer vision approach. The developed system can be deployed in any pathological laboratory to help pathologist by giving support of automated anemia diagnosis and computerized report generation. Advanced image processing algorithm and data mining approach have been used to analysis patient medical information. The pathological data analysis module can process the blood test result to detect anemia type in blood. The image analysis module can identify the abnormal erythrocytes in the smear images using shape based classification. A total number of 38 shape features are extracted from each erythrocyte. Moreover, the supervised decision tree classifier C4.5 is used to classify image samples with sensitivity of 98.1% and specificity of 99.6%. The proposed system will record patient medical information like clinical data, blood test data, and microscopic smear images. Java swing, ImageJ, Weka, Java cryptography extension etc. libraries have been used to develop different applications module of the proposed system.

Keywords—Anemia, computer assisted diagnosis, Java swing application, red blood cell, image processing, shape features; feature selection, classification.

I. INTRODUCTION Nutritional anemia is the most severe public health problem

in India. India leads among the countries with highest prevalence of anemia in the world. According to National Family Health survey (NFHS-3) report [1], the prevalence of anemia is to be 70-80% in children, 56% in women, 59% in pregnant women and 24% in adult men. It is also recorded that about 20-40% of maternal deaths in India due to anemia [2]. Prevalence of anemia in India is high because of low dietary intake, poor availability of iron and chronic blood loss due to hook worm infestation and malaria.

Various factors like nutritional factors, genetic factors, structural defects, disease pathologies may cause abnormalities in shape and size of the erythrocytes. Iron deficiency Anemia is one of the very common reasons behind such cell abnormalities. The visual inception of blood slides can judge those abnormalities but not the reason. However such process can help to provide preliminary status of a disease. Moreover, in anemia the numbers of erythrocytes are less or hemoglobin is less than the normal quantity in blood. Due to abnormal level of hemoglobin in blood cell, the size and shape of the

erythrocyte is also changed. Visual examination of stained blood smear slides under microscope can help to identify the type of anemia based on the morphological structure of cells. The complete blood count (CBC) test helps to give information about the cells in blood. Based on the CBC test result the type anemia can be diagnosed.

Pathological test is one the most standard approach to diagnose the disease like anemia. However the evaluation process of pathological test is very tedious, time-consuming and subjective error prone because a pathologist has to measure the values of the cell parameters manually under light microscope and to make decisions based on these counting. Moreover, pathologist has to write the pathology report manually which is a very tiresome job. To evade such complexity, computer assisted diagnosis (CAD) system or expert system is one of the best options. The use of automated system in pathology is well accepted. Several group of studies on automated computer-assisted method in pathology have been reported. Doctors and pathologist are also appreciated to incorporate the information technology with the manual pathological process. Different blood smear image screening software based on image processing and machine learning has been implemented. Firedman [3] has reported a list of developed computer-assisted clinical systems which are already implemented. Yang et al. [4] have developed an automated pathological diagnostic tool called PathMiner which can classify blood sample. Frejlichowski [5] has used template matching, shape feature description algorithm and polar-fourier grayscale descriptor to identify abnormal cell in microscopic images. Das et al [6] have used invariant moment to recognize abnormal erythrocytes.

The aim of this study is to develop automated anemia diagnosis software which can provide a computer generated pathological report with automated diagnostic result based on the pathological test results and blood smear images. The computer assisted report only identify the abnormal shaped erythrocytes and make decisions about anemia type based on CBC test result. However, the proposed system can’t recognize the reason for anemia. Such system will enhance the present manual process of disease identification from blood sample in pathological laboratory as well as the manual report generation procedure. The proposed method will assist a pathologist in the field of laboratory blood test study and help doctor to diagnosis by giving a detailed well-structured pathological report.

The developed system has been deployed in several hospitals in Medinipur, West Bengal to collect medical

2012 Third International Conference on Emerging Applications of Information Technology (EAIT)

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information and to test the application performance. In the remaining portion of the page, the core functional components of the developed system are described.

II. MATERIALS AND METHODOLOGY The proposed system is developed under Java platform.

Java Swing technology [7] has been used to develop the application whereas ImageJ [8] library is used to develop the image analysis module and WEKA library [9] is used for data mining purpose. Swing approach is based on Model-delegate architecture which is a simplified variant of Model-View Controller (MVC) design [7]. MySQL is used to store patient health information. Java Database Connectivity (JDBC) is used as connector tool between the software and database management application. Moreover, Java Cryptography Extension framework [10] is used to encrypt patient information. iText library [11] is used to generate softcopy of patient report in portable document format(PDF).

Fig. 1. Workflow of the overall diagnosis method

The patient diagnosis process is a three steps process where

at first a doctor examines a patient and suggests some blood tests. Afterwards, a pathologist examines patient’s blood sample and makes a report based on the study. At last doctor diagnoses the patient based on the blood report. In this long diagnosis process, the proposed system will help a pathologist by giving support of making the blood report automatically. The practical scope of this proposed system in patient diagnosis procedure is presented in the Fig. 1.

The overall GUI is designed using Java Foundation classes (JFC) libraries [7]. Each user action is handled by the UI delegate [7] that draws each user interface (component) in the screen and handles GUI events. The schema of the relational database is designed and normalized. Total 18 relation tables are present to manage patient data.

Fig. 2. Data flow diagram of the proposed system

Fig. 2 describes the data flow diagram (DFD) of the developed system. A DFD is graphical representation of the flow of data in a system. Here Pathologist acts as user entity. A pathologists can access the system and do jobs like patient information entry, report generation etc. The core functional processes of the developed software are described below.

A. Patient Information Acquisition Module This module is a combination of graphical user interface

and backend programming. Medical information related fields are given in a user interface and those information will be uploaded into database by pathologist based on patient medical information. The basic demographic parameters like name, age, gender, address, religion, symptoms, past history, family history etc. have been given as field in the information acquisition window. The blood test result information like hematology study, serology study, biochemistry study, microbiology study etc. results can be uploaded into the system. The image upload unit is designed to make it more user-friendly, simple and robust. In this system, the image upload operation can be done in two ways. In the first method, a user can choose multiple numbers of images in any image format from disk-storage. The second option is fully automatic, where the system is connected with the universal serial bus (USB) camera microscope. In this option a user can directly operate the microscope and capture multiple numbers of images which will be automatically uploaded into database. The camera handling operation designed based on the LTI-civil Java library [12,13]. The LTI-civil library stands for Larson Technologies Inc. Capturing Images and Video In a Library, which can detect USB camera device and capture images from the camera.

The size of the information for a single patient will be varied depending on the number of images per patient. The lossy image compression technique i.e. Joint Photographic

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Group (JPEG) [14] has been implemented to reduce the image size by 80% from original size.

B. Blood Test Information Analysis Module Pathological test result information analysis module is

designed to make decision about anemia status from the blood test result. Based on morphology of erythrocytes, anemia can be categorized into four types viz. Microcytic/Normochromic, Macrocytic/Normochromic, Microcytic/Hypochromic and Normocytic/Normochromic, where the name itself signifies the condition of the red blood cells. Table I describes different types of anemia.

TABLE I. TABLE TYPE STYLES

Table Head Table Column Head Microcytic/Hypochromic

Cell size is smaller and color is paler than usual.

Microcytic/Normochromic Cell size is smaller than usual but color is normal.

Macrocytic/Normochromic Cell size is larger than usual but color is normal.

Normocytic/Normochromic Cell size is normal and color is normal as usual

Different CBC test parameters especially Heamoglobin level (Hb) and mean corpuscular volume (MCV) can detect anemia from blood. However the normal ranges of those two parameters change with age and sex. Table II shows the normal range of Hb and MCV.

TABLE II. TABLE TYPE STYLES

Age category Hb level (gm/dl) MCV (fl) Child 11.5-14.5 77-91 Male (Adult) 13.5-18.0 76-96 Female (Adult) 11.5-16.5 76-96

The lesser value of MCV signifies as microcytic and higher value indicates as macrocytic and normal value of MCV suggests normocytic. Therefore, the data analysis module can only partially diagnosis the anemia like Microcytic anemia or Macrocytic anemia or Normocytic anemia or Normal. An algorithm has been designed to classify the pathological data. The flow chart diagram of the developed data analysis algorithm is shown in the Fig 3.

C. Microscopic Blood Image Analysis Module The image analysis module identifies various abnormal

shaped cells present in microscopic blood images. The details of different abnormal shaped cells are shown in Table III. Each step of the image processing is briefly described as follows.

TABLE III. ABNORMAL SHAPED ERYTHROCYTES

Type of Cell Cell Structure Microcytic Large shaped cell Macrocytic Small shaped cell Teardrop Shaped like tear drop Sickle Crescent shaped cell Elliptocyte Elliptical shaped cell

Fig. 3. Flow chart diagram of blood result analysis algorithm

1) Image Retrieve: Blood smear images are captured from leishman stained blood slides by Leica Observer (Leica DM750, Leica Microsystems Ltd.) under 100X oil objective (NA 1.5150). The effective magnification is 1000 times and the corresponding resolution 0.064 micron. However the grabbed images are uploaded in a compressed format into system database by pathologist at very first stage of patient entry. When a pathologist wants to analysis an image, the resultant image will be retrieved from database. The retrieved image will served as input for next steps of the image analysis module.

2) Image Preprocessing: There are significant changes in the illumination of the blood smear images due to light microscopy. Here the rolling ball algorithm [15] has been used for background subtraction. But before applying the algorithm on the image, the colored image is converted into gray scale.

3) Image Segmentation: The global thresholding method i.e. Otsu method [16], is used to threshold the pre-processed image. The Otsu method measured the optimum threshold pixel value and binarized the image where the background is labeled as white and the foreground or objects are labeled as black. But due to intensity variation, some object is not fully represented as black object. The hole filling method [14] has been used to fill the spot in the black object. Afterwards, the watershed algorithm [14] is used to spit the overlapped cells. At last each black object are cropped out from the image and represented as single red blood cell of the original image.

4) Feature Extraction: As the shape based features are considered for the present study, the binary image is considered for the rest. As per pathologist view, shape is one of the most powerful and recommended feature for isolation

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between normal and abnormal cell. Different shape based features are measured from each binary cropped erythrocytes. The lists of all extracted features are shown in the Table III. In this study, total 38 shape features [17] are extracted from segmented blood cell.

5) Classification: After feature extraction, classification is the most important step. In this present study, two supervised classifier is used for classification. The Naïve Bayes’ approach [18] , a simplified version of Bayesian classifier, is used as a first classifier. It is a probabilistic classifier where the class membership probabilities are predicted. The maximum posterior probability is measured to find the particular class of a evidence. The second classifier is decision tree based method called C4.5 . The C4.5 algorithm [18], which derives from simple divide-and-conquer algorithm, produces the decision tree based on the learning dataset. A decision tree is flowchart like tree structure where each internal node signifies a test on an attribute and each branch represents an outcome of the test. Furthermore each leaf node denotes the class label. C4.5 uses gain ratio as splitting criteria

The complete image analysis steps are described in the Fig. 4.

Fig. 4. (A) Orginal image, (B) Gray-scale image, (C) Background corrected

image, (D) Otsu thresholded binary image, (E) Hole filled image, (F) Watersegmented image, (G) Border & small object removed image, (H)

Segemnted blood cells

The above steps are integrated into image analysis module. However for classification, the decision tree classifier is trained by a labeled dataset. The workflow of the image screening module is illustrated in Fig. 5.The steps of the image screening module is described as at first the preprocessing and segmentation process is done. Afterwards each erythrocyte is segmented out and the pre-selected shape features are extracted from them. Each feature set is classified by the classifier and the class of that dataset is determined. Thus, abnormal cells are identified.

Fig. 5. Workflow of the image processing module

D. Report Generation Module The pathological data analysis module and image analysis

module have been developed and integrated with the report

generation module. When a pathologist makes a request for report of a particular patient, both two modules will generate their automated result and those results are pasted on patient report with patient demographic information. The generated report can be saved as softcopy in PDF format or made as hardcopy in printout form.

TABLE IV. EXTRACTED SHAPE FEATURES DETAILS

Feature Mean Std. Dev Min. Max. Perimeter 395.27 75.42 219.14 594.24 Area 9909.76 3625.21 2296 20090 Radius of the inscribed circle 45.07 14.53 6.55 70.1

Radius of the enclosing circle 70.44 17.08 39.25 251.83

Largest axis length (Feret) 135.64 31.25 73.41 237.42

Angle of the Feret 91.26 51.86 3.26 175.21 largest axis perpendicular to the Feret

98.55 26.6 36.8 156.97

Convex Hull 370.92 68.75 206.45 552.08 Area of the Convex Hull 10173.51 3621.69 2517.5 20372

Radius of the Minimal Bounding Circle 67.89 15.6 36.8 118.71

Countcorrect 73.73 107.657 1.44 378.89 Aspect Ratio 1.49 0.63 1 3.95 Circularity 0.78 0.13 0.34 0.91 Roundness 0.71 0.22 0.22 0.98 Area Equivalent Diameter 110.26 21.45 54.07 159.94

Perimeter Equivalent Diameter 3154.37 1153.94 730.84 6394.85

Equivalent Ellipse Area 10552.24 3706.24 2749.68 21837.21

Compactness 0.83 0.14 0.47 0.99 Solidity 0.97 0.03 0.72 0.99 Concavity 263.76 291.137 67 2603.5 Convexity 0.94 0.02 0.81 0.96 Shape 16.71 3.7 13.84 37.3 RFactor 0.88 0.09 0.69 0.99 Modification Ratio 0.69 0.23 0.1 0.96 Sphericity 0.67 0.23 0.09 0.96 Area of Bounding Box 13435.5 4718.93 3501 27804 Rectanularity 0.73 0.05 0.47 0.82 Centroid Distance Mean 67.71 33.69 30.53 427.13

Centroid Distance Variance 1434.45 6008.56 0.37 78675.9

Hu 1 1.71 0.2 0.83 1.84 Hu 2 6.99 2.6 1.84 13.85 Hu 3 10.68 2.59 3.82 20.09 Hu 4 15.19 4.62 4.34 28.53 Hu 5 28.69 8.31 8.42 53.13 Hu 6 19.29 5.97 5.83 33.55 Hu 7 29.16 7.98 10.93 53.24 Eccentricity 2.84 3.11 1.01 21.559 Orientation 7.79 49.22 -89.74 88.82

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E. Security The system is only accessed by the password authentication

property which helps to restrict the unauthorized intrusion. The patient information has been stored into database in encrypted format. The confidentiality of medical information has been strictly maintained in the system. Any person without having the authorization can’t have the permission to access the system. For data encryption, the symmetric key cryptography algorithm11 has been used to encrypt patient information in database.

III. RESULT AND DISCUSSION Different abnormal shaped erythrocytes are classified.

Based on the shape six classes viz. Microcytic, Macrocytic, Sicle, Teardrop, Eliptocyte, and Normal are considered. The classification accuracy of the image analysis module has been performed by the 10 fold cross validation. Before measuring the accuracy, a large labeled dataset of 1500 instances (Normal: 500, Microcytic: 200, Macrocytic: 200, Teardrop: 200, Sicle: 200, Eliptocyte: 2000) has been prepared. The mathematical details of classifier accuracy standards are described below.

Sensitivity = 100%TPTP FN

×+

Specificity = 100%TNTN FP

×+

Precision = 100%TPTP FP

×+

Comparative analysis has been performed between the classifiers. From result, the C4.5 classifier produces better result than Naïve Bayes. The comparative study is presented in the Fig.6 where the classifier accuracy outcomes are measured in different fold of cross-validation. The C4.5 is implemented as classification tool in the proposed method. The classifier accuracy details is shown in Table V using 10 fold cross validation.

Fig. 6. Comparative study of two classifier accuracy measurements

TABLE V. EXTRACTED SHAPE FEATURES DETAILS

Performance measure Result Total instance 1500 Correctly classified 1472 Incoorectly classified 28 Sensitivity 98.1% Specificity 99.6% Precision 98.2%

Fig. 7 shows the graphical view of various cell present is blood smear. These six types of cell are identified by the proposed system.

Fig. 7. Abnormal erythrocytes (A) Teardrop, (B) Sicle, (C) Eliptocyte, (D) Microcytic, (E) Macrocytic, (F) Normal

Fig. 8 presents the graphical interface of the developed system where the figure describes user interface of the image acquisition page. The Fig. 9 is snapshot of the automated computer generated pathological report. In the automated report, patient medical information as well as computer generated result and pathologist suggestion are reported.

Fig. 8. Blood image acquisition user interface

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Fig. 9. Computer generated pathological report

IV. CONCLUSION The proposed system is designed to reduce the cost, time

and labor of generating anemia diagnosis pathological report. The computer-assisted decision can help pathologist to make pathological report quickly.. Moreover, a pathologist can handle a large amount of medical cases with the help of the developed software. A doctor will be benefited by the detailed well-organized report at the time of diagnosis. The system is designed under Java platform which makes the system very robust, light-weighted, secure and cost effective. The system was successfully installed in some hospital & private pathological test center and provided extensive support by making automated decision. The proposed system has many advantages like high diagnosis accuracy, very low cost product, easily compatible with different system etc. Therefore, the system will represent as an intelligent electronic assistant tool in anemia diagnosis field.

Shape-based feature classification can be improved by considering texture and color features of erythrocytes. The process of making decision from CBC test measures is kind of semi-automatic operation because the decision making process depends on the manual CBC test measurements. Therefore there is a scope to build automatic counting software which can automatically measure the CBC test parameters using advanced image processing algorithm and machine learning tools. If the developed system is implemented in web-based framework,

then the system will be accessed globally from anywhere at any time.

ACKNOWLEDGMENT The authors are extremely thankful to Dr. A K Maity,

Midnapur Medical College & Hospital, for his clinical support. Authors acknowledge to DIT, Govt. of India for financial support (Ref. No. IIT/SRIC/SMST/DPR/2009-10/15).

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