2014 Fourth International Conference on Communication Systems and Network Technologies
GUI based Brain Tumor Identification System by Detecting Infected Region through a Combination of Region Growing, Cryptography and Digital Watermarking Technique Koushik Pal
Subhajit Koley
Institute of Radio Physics and Electronics, University of Calcutta Kolkata, India
[email protected]
Electronics & Communication Engineering, Guru Nanak Institute of Engineering Kolkata, India
[email protected]
Mahua Bhattacharya
Goutam Ghosh
Indian Institute of Information Technology and Management, Gwalior, India
[email protected]
Institute of Radio Physics and Electronics, University of Calcutta Kolkata, India
[email protected]
to other parts of the brain and can usually be removed more easily than malignant tumors. Malignant tumors reproduce and grow quickly. Their borders are hard to distinguish from the normal brain around them and hence it is difficult to remove them completely without damaging the surrounding brain. Brain cancers are relatively rare, but they are often fatal. The most common malignant types are called gliomas, where cells called glia (cells which help support the nerve cells) become cancerous. Glioblastoma multiforme is the most common of the gliomas. Glioblastoma multiforme and anaplastic astrocytoma are fast-growing gliomas. Oligodendroglioma, another type of glioma, is also rare, but is most often found in adults. Gliomas make up between 50% to 60% of all brain tumors (malignant and benign) in both children and adults combined. Medullablastoma, which grows from the cells of the medulla at the base of the brain, is the most common type of brain cancer in children. It usually affects children before puberty. Finally, sarcoma and adenocarcinoma are extremely uncommon types of brain tumor [12]. The exact cause of cancer is unknown. Brain cancer that originates in the brain is called a primary brain tumor. It can spread and destroy nearby parts of the brain. Cancers of the lung, skin, or blood cells (leukemia or lymphoma) can also spread (metastasize) to the brain, causing metastatic brain cancer. These groups of cancer cells can then grow in a single area or in different parts of the brain [13]. Brain imaging methods allow neuroscientists to see inside the living brain [14, 15]. These methods help them to understand the relationships between specific areas of the
Abstract—A new type of brain tumor identification system is proposed and analyzed in this paper by detecting Infected Region using a combination of Region Growing Algorithm, Cryptography and Digital Watermarking. The information related to patients contained in the Electronic Patient Record (EPR), Region of Infection (ROI), doctor’s name and diagnosis from symptoms are encrypted and embedded in the tomographic image itself using the proposed methodology - a combination of the Rivest-Shamir-Adelman (RSA) encryption and bit plane slicing watermarking technique. The infected region is identified through region growing and contour detection algorithm which needs to be perfect for accurate ROI identification resulting in a better treatment. The image quality metrics show that the proposed GUI based brain tumor diagnostic system is good enough for successful recovery of all the embedded information and is found to be similar to the embedded information. Keywords- EPR; tomographic image algorithm; ROI; bit plane slicing
I.
region
growing
INTRODUCTION
Brain cancer or brain tumor is one of the most common diseases which have been a matter of deepest concern in recent times and needs early detection and proper treatment to increase the chance of complete recovery. Brain cancer is a tumor or cancerous growth in the brain. A tumor, whether in your brain or elsewhere, is a mass of cells that reproduce themselves in an uncontrolled way. Tumors can be either benign or malignant. Benign brain tumors are abnormal collections of cells that reproduce slowly and usually remain separate from the surrounding normal brain. They grow slowly, do not spread 978-1-4799-3070-8/14 $31.00 © 2014 IEEE DOI 10.1109/CSNT.2014.159
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the region starts to grow. The surrounding pixels or neighbors are compared with the intensity level of the seed point depending on the predefined threshold. This threshold is used to bring the intensity level of the surrounding pixels up to a certain acceptable range. In this process the seed point is compared with the neighboring pixels and it groups the pixels having the same or near intensity level, thus forming the region of interest if they satisfy the homogeneity property of that region. The homogeneity property Prop (R) can be mathematically expressed as follows:
brain and what function they serve and to locate the areas of the brain that are affected by neurological disorders. A tomography image gives a clear idea about the infected region and also discloses whether it is benign or malignant. Medical information regarding cases of cancer, to date, is still a sensitive issue. It needs confidentiality and security so that only authenticated persons should be allowed entry into the information content [1, 2]. Computed Tomography Scan (CT scan): CT scans use a series of X-ray beams passed through the head. The images are then developed on a sensitive film. This method creates cross-sectional images of the brain and shows the structure of the brain, but not its function. Positron Emission Tomography (PET): A scanner detects radioactive material that is injected or inhaled to create an image. Commonly used radioactively-labelled material includes oxygen, fluorine, carbon and nitrogen. This method provides scientists with an idea of the function of the brain. Magnetic Resonance Imaging (MRI): MRI uses the detection of radio frequency signals produced by displaced radio waves in a magnetic field. It provides an anatomical view of the brain. The anatomy of the brain is studied by means of axial, coronal and sagittal views. Cryptography and Watermarking are the technologies that can be used for protection of the patient’s information in the tomography image in encrypted form [3, 4]. Treatment of patients can be carried out at a much reduced cost as they can be treated locally and are not required to travel great distances to obtain the advice of specialist doctors through effective and secured communication between remote hospitals and distant specialist. Hence, the issues relating to unified network protocols and different security settings in the data transfer arise [5]. Transmission of medical information among the geographically separated medical organizations has to be done in a secured way [6]. Biomedical image watermarking offers a solution in this respect for enhancing data security, content verification and image fidelity [7], imperceptibly embedding external data in medical images without changing any information, image size or format [8, 9]. Hiding patient data (EPR) in the medical image is one of the applications of digital image watermarking. EPR contains the health history of a patient that may be in various forms such as patient’s name, age, sex, symptoms and other related information. Protection of EPR in digital health care system is very important [10, 11].
prop( R) :
1 g r, c expg 2 Tth CardR ( r ,c R ) …..(1)
where Card R is the number of pixels in the region R and exp[g] is the mean gray level of pixels lying in that region, i.e. 1 exp[ g ] g r, c CardR ( r ,c R ) …..(2) and Tth is the predefined threshold. This process continues in a recursive manner till the current region is marked with a unique label. II.
PROPOSED METHODOLOGY
The proposed methodology is based on cryptography and watermarking. RSA algorithm is used to encrypt the doctor’s name and diagnosis while bit plane slicing is used to hide the EPR and infected region. First the infected region (ROI) is identified from the tomographic image using region growing and contour detection algorithm [16] and then the ROI is separated and stored as patient’s information. EPR may contain a collection of the patient’s information, viz. name, age, sex, address and symptoms which helps the doctor to diagnose the type and stage of the brain cancer. After that the cover tomography image is divided into two parts, upper part and lower part. Again, the upper part of the cover image is divided into two parts, upper 1 and upper 2. The doctor’s name and diagnosis are next taken as the doctor’s record. Then the upper 1 part is taken and divided into several parts. The RGB color image parts are converted into YCbCr color spaces and color components Y, Cb, Cr are extracted from YCbCr color space images. The doctor’s record is converted into its ASCII value. Then 2 prime numbers are taken to generate a public key and a private key. The doctor’s record is converted into cipher text using RSA algorithm encryption method. The cipher text is embedded into Cb, Cr components of the upper 1 part using LSB replacement method. Then the keys are embedded into the upper 1 part of the cover image. After that upper left and upper right part of the cover image is converted from YCbCr colour space to RGB colour space. Upper 2 part of the cover image is used to hide ROI after taking the bit plane slicing [17]. The ROI is hidden in the 7th bit of upper 2 parts and the EPR is hidden in the 6th bit plane of the lower part once the data has been embedded successfully, all the parts of the
Region Growing Algorithm: Region growing algorithm is used to find the appropriate boundary and also to make a continuous contour. Boundary is the difference between two regions depending on their intensity levels. This is a procedure that groups pixels to form a region based on predefined criteria for growth. Here the intensity or gray level property is used to group the pixels to form an ROI. In this algorithm first a seed point is taken which can be selected from any part of the medical image. This point is called seed point because it is the starting point from where
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The flowchart for proposed embedding process of combined Cryptography – Watermarking is given in Fig. 1 which explains the methodology in detail. In Fig. 2, a screen shot of the proposed methodology is given which describes the outcome of the entire process including ROI identification, embedding and recovery process.
tomographic image are joined and hence the watermarked image is created. At the receiver part all the hidden information can be extracted successfully using the recovery process which follows the reverse steps of the embedding process. After getting the correct keys required for RSA decryption and the dimensions of hidden ROI and EPR from the user information are retrieved. Find infected region through region growing algorithm
Find ROI through Contour Detection algorithm
EPR Split the cover image into different parts
Doctor’s Name
Crop the ROI that is to be embedded
Diagnosis
Doctor’s Information
Dimension of the EPR Dimension of the ROI
Convert it into its ASCII value
8
Flip the ASCII value
7 6 5 4
Public Key
Key
3 2 1
Private Key
Key
Key Generation Bit plane slicing RSA Algorithm
Join the parts to create final watermarked image
Bit plane slicing
Cipher Text
Watermarked image Convert into 8bit binary digit
Watermark for Doctor’s Name
Watermark for Diagnosis
Figure 2. Flowchart of proposed combined Crypto-Watermarking embedding process
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TABLE I. Colour tomographic cover image, hidden and recovered EPR, ROI along with several image quality metrics
Tomographic Cover Image
Encoder Side Hidden Patient’s Information with EPR and ROI
Brain PET Scan
EPR
Watermarked Image with Image Quality Metrics
Diagnosis
Brain Coronal MRI
54.503 0.230 0.996 1.001 0.989 0.793
BER SC SSIM NCC UIQI MI Doctor’s Name Diagnosis
EPR
Dr. Manmeet Ahluwalia Brain Cancer BER SC SSIM NCC UIQI MI
0 1 1 1 1 0. 592
BER SC SSIM NCC UIQI MI
0 1 1 1 1 0.468
Recovered EPR 54.408 0.235 0.998 1 .987 0.814
Infected Region Dr. Anand Kumar
Recovered Infected Region
Doctor’s Name Diagnosis
Brain Cancer
Diagnosis
Doctor’s Name
EPR
Dr. Anand Kumar Brain Cancer BER SC SSIM NCC UIQI MI
0 1 1 1 1 0. 611
BER SC SSIM NCC UIQI MI
0 1 1 1 1 0.400
Recovered EPR PSNR BER SC NCC SSIM UIQI
Doctor’s Name Diagnosis
0 1 1 1 1 0.408
Recovered Infected Region
Infected Region Dr. Manmeet Ahluwalia Brain Cancer
PSNR BER SC NCC SSIM UIQI
Brain Axial CT Scan
Decoder Side Image Quality Metrics for Recovered EPR & ROI 0 BER 1 SC 1 SSIM 1 NCC 1 UIQI 0.617 MI
Recovered EPR PSNR BER SC NCC SSIM UIQI
Doctor’s Name
Recover Patient’s Information with EPR and ROI
55.021 0.204 0.999 1 .989 0.857 Recovered Infected Region
Infected Region Dr. Amrit Saxena Brain Cancer
Doctor’s Name Diagnosis
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Dr. Amrit Saxena Brain Cancer
Superimposed ROI on Watermarked Image
TABLE II. Gray scale tomographic cover image, hidden and recovered EPR, ROI along with several image quality metrics
Tomographic Cover Image
Encoder Side Hidden Patient’s Information with EPR and ROI
Sagittal Brain MRI
EPR
Watermarked Image with Image Quality Metrics
Sagittal Brain MRI
Axial Brain CT Scan
BER SC SSIM NCC UIQI MI
0 1 1 1 1 0.244
Recovered Infected Region
Doctor’s Name Diagnosis
EPR
Dr. A K Jotwani Brain Cancer BER SC SSIM NCC UIQI MI
0 1 1 1 1 0.593
BER SC SSIM NCC UIQI MI
0 1 1 1 1 0.400
Recovered EPR 55.038 0.203 0.997 1.001 0.998 0.980 Recovered Infected Region
Infected Region Dr. P V Reddy Brain Cancer
Doctor’s Name Diagnosis
EPR
Dr. P V Reddy Brain Cancer BER SC SSIM NCC UIQI MI
0 1 1 1 1 0.605
BER SC SSIM NCC UIQI MI
0 1 1 1 1 0.484
Recovered EPR PSNR BER SC NCC SSIM UIQI
Doctor’s Name Diagnosis
57.845 0.106 1.001 0.999 0.999 0.972
Infected Region Dr. A K Jotwani Brain Cancer
PSNR BER SC NCC SSIM UIQI Doctor’s Name Diagnosis
Decoder Side Image Quality Metrics for Recovered EPR & ROI 0 BER 1 SC 1 SSIM 1 NCC 1 UIQI 0.612 MI
Recovered EPR PSNR BER SC NCC SSIM UIQI
Doctor’s Name Diagnosis
Recover Patient’s Information with EPR and ROI
56.342 0.150 0.999 1 0.990 0.816 Recovered Infected Region
Infected Region Dr. Santanu Sen Brain Cancer
Doctor’s Name Diagnosis
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Dr. Santanu Sen Brain Cancer
Superimposed ROI on Watermarked Image
III.
RESULTS AND DISCUSSION
It is observed in Tables I and II, three sets of color and three sets of gray scale tomographic cover images, identified infected region from the cover images, EPR, doctor’s name and diagnosis are given in the encoding portion. After hiding all this information, the obtained watermarked images are also given. The values of several image quality metrics are also included to test the quality of the encoding process [18]. A high value of PSNR (Peak Signal to Noise Ratio) and the values of SC (Structural Content), SSIM (Structural Similarity Index), UIQI (Universal Image Quality Index) and NCC (Normailzed Cross Correlation) indicates the good quality of the embedding process with very low distortion as the value of BER (Bit Error Rate) is quite small. The decoder portion is the outcome of the recovery process where different recovered information are given. It can be clearly observed that all the encoded information like EPR information, diagnosis, doctor’s name and even the embedded infected region can be successfully recovered and appears similar to the hidden one. The values of some important image quality metrics also support the quality and strength of the recovery process statistically. The value of BER is 0 and SC, SSIM, NCC and UIQI are 1 which indicates that the recovered ROI and EPR are exactly same as that of the embedded one. IV.
[2]
[3] [4]
[5]
[6]
[7]
[8]
[9] [10]
CONCLUSION
Biomedical tomographic images are sensitive in nature which may be incorporated in the EPR. Thus the EPR is very important as it contains the detailed patient’s information and ROI. Much care has to be taken when embedding and extracting the information. Wrong detection of ROI and erroneous retrieval of EPR can hamper the diagnosis which can lead to a wrong treatment with fatal results. This paper describes a new type of biomedical EPR authentication system which is a combination of RSA encryption technique and bit plane slicing watermarking. Multiple information are encrypted and embedded using the proposed technique and the process by which they may be recovered. A number of image quality metrics are used to find the quality degradation of the watermarked image and recovered ROI. The value of these image quality metrics can established the fact that the proposed combined Cryptography Watermarking technique is quite efficient and can successfully retrieve all the embedded information. Therefore it can be concluded that the proposed authentic and secured GUI based brain tumor identification and diagnostic system can be used as a secure data hiding, and recovery system, ideal for modern health information network.
[11]
[12]
[13]
[14]
[15]
[16]
[17]
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