-
[show abstract]
[hide abstract]
ABSTRACT: In this work, we have developed and evaluated an electrocardiogram (ECG) feature extraction system based on the multi-resolution wavelet transform. ECG signals from Modified Lead II (MLII) are chosen for processing. The result of applying two wavelet filters (D4 and D6) of different length on the signal is compared. The wavelet filter with scaling function more closely similar to the shape of the ECG signal achieved better detection. In the first step, the ECG signal was de-noised by removing the corresponding wavelet coefficients at higher scales. Then, QRS complexes are detected and each complex is used to locate the peaks of the individual waves, including onsets and offsets of the P and T waves which are present in one cardiac cycle. We evaluated the algorithm on MIT-BIH Database, the manually annotated database, for validation purposes. The proposed QRS detector achieved sensitivity of 75 . 2 % 18 . 99 ± and a positive predictivity of 45 . 4 % 00 . 98 ± over the validation database.
-
[show abstract]
[hide abstract]
ABSTRACT: Ultrasound imaging has become an effective imaging system in medicine. The ultrasound images are generally created in two-dimensional format; however three dimensional images offer better observation and interpretation. To create three-dimensional images, special transducers with two dimensional arrays should be used. These transducers consist of piezoelectric elements arranged in two dimensions. Since the construction of such two dimensional transducers are complex, simulation of these transducers is an appropriate way to analyse and optimize their beam pattern for a particular application. In this study, we first derived all the equations related to the construction of beam pattern of the transducers and then simulated them using Matlab. User-friendly software has been developed to enable user to alter the key parameters of the beam pattern such as beam width, side lobes, grating lobes and to find the optimized beam pattern for a particular application. The optimization is based on element sizes, element spacing, apodization and excitation pulses. The result of the beam pattern optimization can be shown graphically and analyzed efficiently. This study can be used as an appropriate tool for the design of one dimensional and two dimensional array transducers.
-
[show abstract]
[hide abstract]
ABSTRACT: Oral contrast is usually administered in most X-ray computed tomography (CT) examinations of the abdomen and the pelvis as it allows more accurate identification of the bowel and facilitates the interpretation of abdominal and pelvic CT studies. However, the misclassification of contrast medium with high density bone in CT-based attenuation correction (CTAC) is known to generate artifacts in the attenuation map (mumap), resulting in overcorrection for attenuation of PET images. In this paper, we developed an automated segmentation algorithm for classification of regions containing oral contrast medium in order to correct for artifacts in CT attenuation-corrected PET images using the segmented contrast correction (SCC) technique. Our segmentation algorithm consists of two steps: (1) high CT number object segmentation using combined region- and boundary-based segmentation and (2) object classification to bone and contrast agent based on fuzzy classifier as knowledge-based nonlinear classifier. Thereafter, the CT numbers of pixels belonging to the region classified as contrast medium are substituted with their equivalent effective bone CT numbers based on the SCC algorithm. The generated CT images were down-sampled and followed by Gaussian smoothing to match the resolution of PET images. A bi-linear calibration curve was used to convert CT pixel values in HU to mumap at 511 keV. The visual assessment of segmented regions in clinical CT images performed by an experienced radiologist confirmed the accuracy of the segmentation algorithm for delineation of contrast enhanced regions. The mean attenuation coefficient of a small region in the generated mumaps before and after correction using the SCC algorithm was 0.151 and 0.098 cm<sup>-1</sup>, respectively. Quantitative analysis of generated mumaps from a clinical dataset showed an overestimation of 19.7% of attenuation coefficients in the 3D regions classified as contrast agent. A clinical PET/CT study known to be problem-
atic demonstrated the applicability of the technique. More importantly, correction of oral contrast artefacts improved the readability and interpretation of the PET scan and showed substantial decrease of the SUV (104.3%) after correction. In conclusion, we developed an automated segmentation algorithm for classification of irregular shapes of regions containing contrast medium usually found in clinical CT images for wider applicability of the SCC algorithm for correction of oral contrast artefacts in CTAC. The algorithm is being refined and further validated in clinical setting.
Nuclear Science Symposium Conference Record, 2007. NSS '07. IEEE;
-
[show abstract]
[hide abstract]
ABSTRACT: In this paper we proposed a new method for texture classification of ultrasonic liver images based on Gabor wavelet. It is well known that Gabor wavelets attain maximum joint space-frequency resolution which is highly significant in the process of texture extraction in which the conflicting objectives of accuracy in texture representation and texture spatial localization are both important. This fact has been explored in our results as it shows that the classification rate obtained by Gabor wavelet is higher that those obtained using dyadic wavelets. The feature vector consists of 10 elements at each scale from Gabor wavelets which is relatively small compared to other methods. This has a significant impact on the speed of retrieval process. The proposed algorithm applied to discriminate ultrasonic liver images into three disease states that are normal liver, liver hepatitis and cirrhosis. In our experiment 45 liver sample images from each three disease states which already proven by needle biopsy were used. We achieved the sensitivity 85% in the distinction between normal and hepatitis liver images and 86% in the distinction between normal and cirrhosis liver images. Based on our experiments, the Gabor wavelet is more appropriate than dyadic wavelets for texture classification as it leads to higher classification accuracy.
Signal Processing, 2004. Proceedings. ICSP '04. 2004 7th International Conference on;
-
[show abstract]
[hide abstract]
ABSTRACT: We proposed an automated segmentation of suspicious clustered microcalcifications on digital mammograms. The algorithm consists three main processing steps for this purpose. In the first step, the improvement of the in microcalcifications appearance by using the "a trous wavelet" transform which could enhance the high-frequency content of breast images were performed. In the second step, individual microcalcifications were segmented using wavelet histogram analysis on overlapping subplanes. Then, the extracted histogram features for each subplane used as an input to a fuzzy rule-based classifier to identify subimages containing microcalcifications. In the third step, subtractive clustering was applied to assign individual microcalcifications to the closest cluster. Finally, features of each cluster were used as input to another fuzzy rule-based classifier to identify suspicious clusters. The results of the applied algorithm for 47 images containing 16 benign and 31 malignant biopsy cases showed a sensitivity of 87% and the average of U. 5 false positive clusters per image.
Signal Processing, 2004. Proceedings. ICSP '04. 2004 7th International Conference on;
