Segmentation and Classification of Lung Nodule in Chest Radiograph Image

INTERNATIONAL JOURNAL FOR TRENDS IN ENGINEERING & TECHNOLOGY VOLUME 4 ISSUE 2 – APRIL 2015 - ISSN: 2349 - 9303

Segmentation and Classification of Lung Nodule in Chest Radiograph Image Agalya A1

Nirmalakumari k2

PG scholar, Dept.of ECE, Bannari Amman Institute of Technology, Sathyamangalam,Tamilnadu,India agalya6492@gmail.com

Assistant Professor(Sr.G), Dept.of ECE, Bannari Amman Institute of Technology, Sathyamangalam,Tamilnadu,India nirmalakumarik@bitsathy.ac.in

Abstract-Image segmentation plays a vital step in medical image processing. Lung cancer is the largest cause of tumor deaths.

Since the nodules are commonly attached to blood vessels, detection of lung nodules is the challenging task .By early detection the lung cancer can be completely recovered. Especially in the case of lung nodule detection Computer Aided Detection (CAD) is effective for the improvement of radiologists‟ diagnosis. In this paper an efficient lung nodule detection scheme is developed by performing nodule segmentation through Fuzzy C-Means (FCM) and Virtual Dual Energy (VDE). Here the input image is considered as an radiograph image, then the lung is segmented by using Multi segment Active Shape Model (MASM). Finally neural network classifies as a nodule or non-nodule candidates.

Keywords: Chest Radiography (CXR), Computer Aided Diagnosis (CAD), Fuzzy C-Means (FCM), Virtual Dual Energy (VDE), Multi Segment Active Shape Model (M-ASM).

1 INTRODUCTION A wide variety of imaging techniques is currently available in the field of medical diagnosis, such as radiography, computed tomography (CT) and magnetic resonance. Chest radiography is the most common type of procedure for the initial detection and diagnosis of lung cancer, due to its economic considerations and radiation dose. Lung cancer is the uncontrolled growth of abnormal cells that start off in one or both lungs; one of the most dangerous problem in this world is cancer. In 2005, the five-year survival rates for men and women diagnosed with lung cancer were 13.6% and 17.2%, respectively [1]. If the early diagnosis has become established, then it will be effective. In this paper CXRs is used because it is more effective when compared to all other radiograph techniques [2], [3]. On initial reading of chest radiograph 30% of pulmonary nodules are missed due to overlapping of ribs and clavicles [4], [5]. Dual-energy radiography system is used only in limited hospitals because it is a hardware technique. So we are introducing a software technique called Virtual Dual-Energy (VDE) radiography, developed by Suzuki for the suppression of ribs and clavicles in CXRs .

There are 2 major types of lung cancer:  Small cell lung cancer (SCLC)  Non-small cell lung cancer (NSCLC).

Technology (JSRT). The images were digitized with a matrix size of 2048 x 2048, and 4096 grey levels. For detection of lung nodules in CXRs our original CAD scheme consists of three major steps:1) Segmentation of lung based on Multi segment Active Shape Model (M-ASM) 2) VDE with two-stage nodule enhancement and detection of nodule 3) segmentation of nodule candidates by use of our clustering watershed segmentation algorithm.

3 BLOCK DIAGRAM

2 MATERIALS AND METHOD The method has been developed and tested on a standard database acquired by the Japanese Society of Radiological

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Fig. 1. Block diagram of CAD scheme with the VDE technology

INTERNATIONAL JOURNAL FOR TRENDS IN ENGINEERING & TECHNOLOGY VOLUME 4 ISSUE 2 – APRIL 2015 - ISSN: 2349 - 9303 In this above block diagram, chest radiograph image is taken as an input and it is applied to Fuzzy C-Means for nodule detection.

After detecting the nodule using FCM, lung is segmented using Multi segment Active Shape Model (M-ASM). From this segmented lung image Virtual Dual Energy technique is applied for nodule detection. Comparatively VDE gives the better result than FCM for nodule detection. Finally classification is done using Neural Network.

5 M-ASM TECHNIQUE M-ASM technique is much better than ASM. Because in ASM, it takes each and every point manually so this is not good for lung segmentation. But in M-ASM it takes each and every point automatically based on intensity. So for easy process and time consumption Multi segment Active Shape Model (M-ASM) is applied.

M-ASM technique segments only the lung part.

Fig. 2. Original CXR image

4 FCM FOR THE LUNG NODULE DETECTION Fuzzy C-Means (FCM) is the most popular clustering algorithm for medical image segmentation. It is also a spongy segmentation method. It is a method of clustering which allows one section of data to two or more clusters. In the standard FCM, the centers are initialized arbitrarily and the measure of membership only uses the gray feature. This leads to be sensitive to noise and reasonably time-consuming. Fuzzy C-Means allows us to decrease the uncertainity of pixels belonging to one class and therefore in general provides improved segmentation. In addition, multiple classes with varying degrees of membership can be endlessly updated. The first specific formulation of Fuzzy C-Means (FCM) is credited to Dunn [6]. But its generalization and current framing is designed by Bezdek [7].

Fig. 3. FCM Result

Fig. 4. Lung Segmentation using an M-ASM

6 VIRTUAL DUAL ENERGY TECHNIQUE FOR NODULE DETECTION By using Virtual Dual Energy (VDE) radiography technique, the rib and soft tissue components of the CXRs gets separated. This software technique is applied due to some of the benefits dose is required for the patients. They are:  

For the patients, there is no additional radiation dose is required.

For the generation of the VDE images, there is no need of any specialized equipment.

To detect the lung nodules and to reduce the frequent false positives (FPs) caused by ribs, virtual dual energy radiography technique is used [10] . The ribs are long curved bones that form the cage. Ribs surround the chest, enabling the lungs to expand and facilitate breathing by expanding the chest cavity. In VDE, smoothing process is done by Gaussian kernel function. Virtual dual energy consists of two methods: Two stage enhancement technique and Watershed based segmentation. By using two stage enhancement technique, the blurness can be avoided by selecting the variance value as minimum. With the watershed segmentation rough nodule candidate region was divided into several catchment basins.

Dual energy is used to improve the performance of digital radiograph image with two intensity energy levels that is nodules and normal image from that soft tissue is easily identified. The various types of grey-level morphological operators is used. The term morphology refers to the shape or morphology of features in an image. Disk shaped structural element is used in this paper. Erosion, dilation, opening, and closing are the basic morphological operations. But in this paper only Dilation, erosion and opening have used. Erosion is nothing but it shrinks the image. Dilation process is to expand the image.

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INTERNATIONAL JOURNAL FOR TRENDS IN ENGINEERING & TECHNOLOGY VOLUME 4 ISSUE 2 – APRIL 2015 - ISSN: 2349 - 9303

Fig. 8. Nodule message box

7 COMPARISON OF TWO SEGMENTATIONS

Fig. 5. Erosion result

In segmentation, Virtual Dual Energy technique is better when compared Fuzzy C-Means. VDE gives accurate result with improved sensitivity and specificity.

8 CONCLUSION In this paper for the detection of lung nodule a CADs scheme was developed in chest radiograph image. Virtual dual energy technique is better when compared to Fuzzy C-Means in terms of nodule detection. It shows the results as „Nodule‟ or „Non-Nodule‟ image by depending on the nodule message box.

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Fig. 6. Dilation result

Fig. 7. Watershed segmentation

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