Showing papers in "Iranian Journal of Medical Physics in 2006"

A Novel Blind Watermarking of ECG Signals on Medical Images Using EZW Algorithm

Abstract: Introduction:In this study, ECG signals have been embedded into medical images to create a novel blind watermarking method. The embedding is done when the original image is compressed using the EZW algorithm. The extraction process is performed at the decompression time of the watermarked image. Materials and Methods: The multi-resolution watermarking with a secret key algorithm developed in this work is blind because during the EZW decoding only the secret key which includes the header information is used to extract the embedded information. There are two processes added to EZW coder including insertion process and extraction process. Results: Several grey scale MRI and CT images of size 256 x 256 and 512 x 512 pixels have been watermarked by embedding five different watermarks of size 512, 1024, 2048, 4096, 8192 bytes. The used images were divided into three categories such as MRI, CT and Mammography. Also they are in the size of 256×256 and 512×512. The ECG signal is one kind of normal ECG signal that has been utilized for experiments. In this experiment, the PSNR obtained between the original image and the compressed watermarked was found to be greater than 35 dB for all medical images from different modalities. This finding was also true for the PSNR between the original mark signal and its reconstruction. The β measured for the watermarked image was greater than 0.7 and for most of the images it was greater than 0.9. Discussion and Conclusion: The achieved PSR and β prove the imperceptibility of the method in addition to preserving the edges of the host image. The proposed method is able to utilize about 15% of the host image to embed mark signal that is suitable for most watermarking methods. The proposed method of selecting the insertion sites ensures three watermarking constraints: the Imperceptibility and Robustness, High percentage of watermark and Blindness.

Classification of endometrial images for aiding the diagnosis of hyperplasia using logarithmic gabor wavelet

Abstract: Introduction: The process of discriminating among benign and malignant hyperplasia begun with subjective methods using light microscopy and is now being continued with computerized morphometrical analysis requiring some features. One of the main features called Volume Percentage of Stroma (VPS) is obtained by calculating the percentage of stroma texture. Currently, this feature is calculated by pathologists on a manual basis. The proposed algorithm can automatically calculate VPS with a good precision. This procedure could be the first and an essential step of diagnosing endometrial hyperplasia in the field of pathology. Materials and Methods: In this paper, a method based on logarithmic Gabor wavelets and mathematical morphology is proposed for the segmentation of texture. Attaining maximum joint space-frequency resolution is highly significant in the process of texture segmentation. The ability of logarithmic Gabor filters in the discrimination of texture bands at different scales and orientations has been used to segment stroma texture which is mainly located in the middle frequency bands from glandular elements. In the proposed algorithm, the logarithmic Gabor filter bank applied on the microscopic endometrial images and mathematical morphology is used for denoising. The segmentation method is averaging the weighted and denoised Gabor filter outputs. Results: The images used in this method segment with a good precision and can be used by pathologists to calculate volume percentage of stroma as an aided diagnosis feature. A sensitivity of 95.3, specificity of 95.6, accuracy of 95.4%, PPV of 98.4% and NPV of 88% was achieved in distinguishing between benign and malignant hyperplasia based on VPS. Discussion and Conclusion: The proposed procedure could be the first and an essential step of diagnosing endometrial hyperplasia in pathology.Based on these experiments, the logarithmic Gabor wavelet is one of the most effective methods in texture segmentation that can easily extract the texture information from the middle frequency bands and make it available to the segmentation algorithm.