Abstract— Data Embedding has wide range of applications in the medical field. This method is helpful in securing information of the patients from the intruders with high storage capacity. The medical images of different modalities like CT, MRI, PET, with the digitized clinical data can be sent to the physicians across the world for the diagnosis. Due to the bandwidth and storage constraint, medical images along with the clinical data must be compressed before transmission and storage. This paper presents a new technique for data embedding and retrieving the digitized clinical data along with the DICOM images by using Complex Contourlet Transform (CCT). This paper also estimates the compression method by using Encoder. Hence, this paper suggest that the data embedding and retrieving technique based on Complex Contourlet Transform (CCT) is having high hiding capacity by ensuring the improved values of Compression Ratio (CR), Space Saving (SS), Mean Square Error (MSE) and Peak Signal to Noise Ratio(PSNR).
Keywords—Data Embedding; Data retrieving; CCT; Encoder; Compression.
Introduction
In the last few years, there has been a considerable increase in the volume of data embedding in the medical image in hospitals. Due to the properties of an image, the medical information is different from the other normal information. Moreover, Confidentiality and security of medical data is crucial to protect it from accidental or malicious alteration during the interchange and storage. The medical information require large amount of memory storage and transmission bandwidth in telemedicine applications. Thus, hospitals must deal with very high storage requirements. This gives rise for data embedding technique to reduce the space in hospital digital database.
A number of image processing tasks are efficiently carried out in a domain other than the pixel domain, often by means of an invertible linear transformation. The main challenge in exploring geometry in images comes from the discrete nature of the data. Thus, unlike other approaches such as wavelet, which first develop a transform in the continuous domain and then discrete for sampled data. In Complex Contourlet Transform (CCT) it starts with a discrete domain construction and then studies its convergence to an expansion in the continuous domain. Specifically, it constructs a discrete domain multi-resolution and multi-direction expansion using non-separable filter banks, in which the same way that wavelets were derived from filter banks. This construction results in a flexible multi-resolution, local and directional image expansion using contour segments, and hence it is named as Complex Contourlet Transform (CCT).
Data compression is widely used in all applications like huge data storage, carrying and retrieval such as for multimedia, documents, video conferencing and medical imaging. The main aim of the data compression technique is to reduce the redundancy for storage or to transmit the data in an efficient way. Hence, this results in the reduction of the file size in hospital digital database. The block diagram of the data encoding and decoding is shown in Fig. 1 and Fig. 2
Fig. 1: Block Diagram of Data Encoding
Fig. 2: Block Diagram of Data Decoding
The block diagram consists of three closely components namely Source Encoder, Quantizer and Entropy Encoder. Source Encoder is a variety of linear transforms, Quantizer is a many to one mapping which is the lossy process and it is the main source of compression in an encoder and Entropy encoder will compresses the quantized values losslessly to provide us the better compression technique. The most commonly used entropy encoders are Huffman Encoder (HE), Arithmetic Encoder (AE) and Run-Length Encoding (RLE). To provide the good compression a well designed quantizer and entropy encoder is highly important.
The performance of the proposed method has been compared with all the three entropy encoder mentioned earlier and the quality measurement similar to Peak Signal to Noise Ratio (PSNR), Compression Ratio (CR) and Space Saving (SS) have been estimated to decide the quality of the compressed image.
MATERIALS AND METHODS
The proposed method is implemented by using the Complex Contourlet Transform (CCT) for data embedding and data retrieving.
Complex Contourlet Transform (CCT): In this work, a new approach is performed using CCT which reduces the computational complexity and it improves the image quality. It has basically two major steps, namely dual-tree complex wavelet transform (DT-CWT) for multi-resolution decomposition stage level which gives six directional sub-bands on each scale of the detail coefficient sub-space, each individual has a real and an imaginary part of the wavelet coefficients. Second stage is directional filter bank (DFB) for multi-directional decomposition to group the locally correlated coefficients which is captured by DT-CWT. In the obtained transform, CCT combines the characteristics of NSCT (multi-resolution, localization, directionality and anisotropy) and DT-CWT (translation invariant, directionality). Therefore, it is computationally more efficient than the other transform methods. The level 1 stage decomposition of CCT is shown in Fig. 3
Fig. 3: Decomposition Stage for Level 1 CCT
Proposed Method: In the proposed method an algorithm for data embedding and data retrieving is mentioned with an example.
Data Embedding:
1. Choose the medical cover image and decompose it by Complex Contourlet Transform (CCT)
2. Select the scaling factor or simply called threshold value for a better transmission purpose and also for data security
3. Implement the Spread spectrum embedding process by generating the Pseudo Noise Sequence to embed the message bit in the Coefficient obtained by CCT decomposition process for medical cover image
4. Embedding Data = CCT Coefficient + Scaling Factor * Message bit to be embed
5. Repeat the steps 3 and 4 until all the message bit is embedded
6. Apply Inverse CCT to obtain the final Embedded Image
Data Retrieving:
1. Apply the malicious attacks to the embedded image
2. Apply the Compression technique to the attacked embedded image
3. Select the scaling factor or threshold value for retrieving the data embedded
4. Generate the Pseudo Noise Sequence
5. Retrieved Data = (Compressed Image – Complex Contourlet Coefficient) / scaling factor
6. Repeat the steps 4 and 5 until all the data is retrieved
7. Apply the Inverse CCT to reconstruct the original medical cover image
In the proposed data embedding and data retrieving process scaling factor or the threshold value is chosen in such way that no intruders can steal the patient important information. If scaling factor is very less in value then it will have a better invisible embedding process. The attacks applied in the methods are Gaussian Noise, Salt & Pepper Noise, Sharpening, Rotating, Histogram Equalization and Median Filter.