Quality improvement of digitized radiographs by filtering technique development based on morphological transformations

Amir, Mohammad; Taheri, M.; Kargarnovin, M. H.; Ghasemi, Farshad; Rokrok, B.; Seiedi, M.; Edaalati, K.; Kermani, Ali; Rastkhah, N.

doi:10.1109/nssmic.2004.1462603

Cited by 4 publications

(3 citation statements)

References 7 publications

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“…Wiener filtering is a nonlinear operation often used for reducing the noise and preserve edge simultaneously [28], [29]. The image is viewed as a surface, with mountain (high intensity) and valleys (low intensity).…”

Section: Segmentation Of Acetabular and Femoral Head Cartilagesmentioning

confidence: 99%

“…We then segment the pelvic bones, i.e., the femoral head and acetabulum based on a vector quantization technique [21]- [27]. The articular cartilages are roughly segmented using a top-hat filter and Otsuádaptive thresholding [28], [29]. We then employ the roughly segmented cartilages and articular central line to segment the hip cartilages from each other.…”

Section: Introductionmentioning

confidence: 99%

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A Hybrid Technique for Thickness-Map Visualization of the Hip Cartilages in MRI

Khanmohammadi

Zoroofi

Nishii

et al. 2009

IEICE Trans. Inf. & Syst.

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SUMMARYQuantification of the hip cartilages is clinically important. In this study, we propose an automatic technique for segmentation and visualization of the acetabular and femoral head cartilages based on clinically obtained multi-slice T1-weighted MR data and a hybrid approach. We follow a knowledge based approach by employing several features such as the anatomical shapes of the hip femoral and acetabular cartilages and corresponding image intensities. We estimate the center of the femoral head by a Hough transform and then automatically select the volume of interest. We then automatically segment the hip bones by a self-adaptive vector quantization technique. Next, we localize the articular central line by a modified canny edge detector based on the first and second derivative filters along the radial lines originated from the femoral head center and anatomical constraint. We then roughly segment the acetabular and femoral head cartilages using derivative images obtained in the previous step and a top-hat filter. Final masks of the acetabular and femoral head cartilages are automatically performed by employing the rough results, the estimated articular center line and the anatomical knowledge. Next, we generate a thickness map for each cartilage in the radial direction based on a Euclidian distance. Three dimensional pelvic bones, acetabular and femoral cartilages and corresponding thicknesses are overlaid and visualized. The techniques have been implemented in C++ and MATLAB environment. We have evaluated and clarified the usefulness of the proposed techniques in the presence of 40 clinical hips multi-slice MR images.

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Section: Segmentation Of Acetabular and Femoral Head Cartilagesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Hybrid Technique for Thickness-Map Visualization of the Hip Cartilages in MRI

Khanmohammadi

Zoroofi

Nishii

et al. 2009

IEICE Trans. Inf. & Syst.

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“…2A). Top-hat and bottom-hat morphological filters are utilized to reduce the large and high gray level clusters and to enhance the edges of subcellular structures (Movafeghi et al, 2004;Fig. 2B).…”

Section: Feature Generation and Classificationmentioning

confidence: 99%

Automated recognition system to classify subcellular protein localizations in images of different cell lines acquired by different imaging systems

Tsai

Chung²,

Simpson

et al. 2007

Microscopy Res & Technique

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Systemic analysis of subcellular protein localization (location proteomics) provides clues for understanding gene functions and physiological condition of the cells. However, recognition of cell images of subcellular structures highly depends on experience and becomes the rate-limiting step when classifying subcellular protein localization. Several research groups have extracted specific numerical features for the recognition of subcellular protein localization, but these recognition systems are restricted to images of single particular cell line acquired by one specific imaging system and not applied to recognize a range of cell image sources. In this study, we establish a single system for automated subcellular structure recognition to identify cell images from various sources. Two different sources of cell images, 317 Vero (http://gfp-cdna.embl.de) and 875 CHO cell images of subcellular structures, were used to train and test the system. When the system was trained by a single source of images, the recognition rate is high and specific to the trained source. The system trained by the CHO cell images gave high average recognition accuracy for CHO cells of 96%, but this was reduced to 46% with Vero images. When we trained the system using a mixture of CHO and Vero cell images, an average accuracy of recognition reached 86.6% for both CHO and Vero cell images. The system can reject images with low confidence and identify the cell images correctly recognized to avoid manual reconfirmation. In summary, we have established a single system that can recognize subcellular protein localizations from two different sources for location-proteomic studies. studies.

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