Currently, image-based 3-dimentional (3D) planning brachytherapy allows for a better assessment of gross tumor volume (GTV) and the definition and delineation of target volume in cervix cancer. In this study, we inves...Currently, image-based 3-dimentional (3D) planning brachytherapy allows for a better assessment of gross tumor volume (GTV) and the definition and delineation of target volume in cervix cancer. In this study, we investigated the feasibility of our novel computed tomography (CT)-guided free-hand high-dose- rate interstitial brachytherapy (HDRISBT) technique for cervical cancer by evaluating the dosimetry and preliminary clinical outcome of this approach. Dose-volume histogram (DVH) parameters were analyzed according to the Gynecological GEC-ESTRO Working Group recommendations for image-based 3D treatment in cervical cancer. Twenty cervical cancer patients who underwent CT-guided free-hand HDRISBT between March 2009 and June 2010 were studied. With a median of 5 (range, 4-7) implanted needles for each patient, the median dose of brachytherapy alone delivered to 90% of the target volume (D90 ) was 45 (range, 33-54) Gyα/β10 for high-risk clinical target volume (HR-CTV) and 30 (range, 20-36) Gyα/β10 for intermediate-risk clinical target volume (IR-CTV). The percentage of the CTV covered by the prescribed dose (V100 ) of HR-CTV with brachytherapy alone was 81.9%-99.2% (median, 96.7%). With an additional dose of external beam radiotherapy (EBRT), the median D90 was 94 (range, 83-104) Gyα/β10 for HR-CTV and 77 (range, 70 -87) Gyα/β10 for IR-CTV; the median dose delivered to 100% of the target volume (D100 ) was 75 (range, 66-84) Gyα/β10 for HR-CTV and 65 (range, 57-73) Gyα/β10 for IR-CTV. The minimum dose to the most irradiated 2 cc volume (D2cc ) was 73-96 (median, 83) Gyα/β3 for the bladder, 64-98 (median, 73) Gyα/β3 for the rectum, and 52-69 (median, 61) Gyα/β3 for the sigmoid colon. After a median follow-up of 15 months (range, 3 -24 months), two patients experienced local failure, and 1 showed internal iliac nodal metastasis. Despite the relatively small number of needles used, CT-guided HDRISBT for cervical cancer showed favorable DVH parameters and clinical outcome.展开更多
This paper presents an improved rate control method for H.264. First, the scene changes are detected by the average absolute difference of the brightness histograms between the adjacent frames. Then, the bit allocatio...This paper presents an improved rate control method for H.264. First, the scene changes are detected by the average absolute difference of the brightness histograms between the adjacent frames. Then, the bit allocation and quantization parameters are adjusted, using a certain threshold. In addition, the calculation of the mean absolute difference （MAD） is modified in an alternative way, which makes the rate distortion optimization （RDO） more accurate. Extensive simulation results show that the proposed method, compared with G012, can improve the average peak signal-to-noise ratio （PSNR） and moderate the image quality.展开更多
Lung Cancer is one of the hazardous diseases that have to be detected in earlier stages for providing better treatment and clinical support to patients.For lung cancer diagnosis,the computed tomography(CT)scan images ...Lung Cancer is one of the hazardous diseases that have to be detected in earlier stages for providing better treatment and clinical support to patients.For lung cancer diagnosis,the computed tomography(CT)scan images are to be processed with image processing techniques and effective classification process is required for appropriate cancer diagnosis.In present scenario of medical data processing,the cancer detection process is very time consuming and exactitude.For that,this paper develops an improved model for lung cancer segmentation and classification using genetic algorithm.In the model,the input CT images are pre-processed with the filters called adaptive median filter and average filter.The filtered images are enhanced with histogram equalization and the ROI(Regions of Interest)cancer tissues are segmented using Guaranteed Convergence Particle Swarm Optimization technique.For classification of images,Probabilistic Neural Networks(PNN)based classification is used.The experimentation is carried out by simulating the model in MATLAB,with the input CT lung images LIDC-IDRI(Lung Image Database Consortium-Image Database Resource Initiative)benchmark Dataset.The results ensure that the proposed model outperforms existing methods with accurate classification results with minimal processing time.展开更多
文摘Currently, image-based 3-dimentional (3D) planning brachytherapy allows for a better assessment of gross tumor volume (GTV) and the definition and delineation of target volume in cervix cancer. In this study, we investigated the feasibility of our novel computed tomography (CT)-guided free-hand high-dose- rate interstitial brachytherapy (HDRISBT) technique for cervical cancer by evaluating the dosimetry and preliminary clinical outcome of this approach. Dose-volume histogram (DVH) parameters were analyzed according to the Gynecological GEC-ESTRO Working Group recommendations for image-based 3D treatment in cervical cancer. Twenty cervical cancer patients who underwent CT-guided free-hand HDRISBT between March 2009 and June 2010 were studied. With a median of 5 (range, 4-7) implanted needles for each patient, the median dose of brachytherapy alone delivered to 90% of the target volume (D90 ) was 45 (range, 33-54) Gyα/β10 for high-risk clinical target volume (HR-CTV) and 30 (range, 20-36) Gyα/β10 for intermediate-risk clinical target volume (IR-CTV). The percentage of the CTV covered by the prescribed dose (V100 ) of HR-CTV with brachytherapy alone was 81.9%-99.2% (median, 96.7%). With an additional dose of external beam radiotherapy (EBRT), the median D90 was 94 (range, 83-104) Gyα/β10 for HR-CTV and 77 (range, 70 -87) Gyα/β10 for IR-CTV; the median dose delivered to 100% of the target volume (D100 ) was 75 (range, 66-84) Gyα/β10 for HR-CTV and 65 (range, 57-73) Gyα/β10 for IR-CTV. The minimum dose to the most irradiated 2 cc volume (D2cc ) was 73-96 (median, 83) Gyα/β3 for the bladder, 64-98 (median, 73) Gyα/β3 for the rectum, and 52-69 (median, 61) Gyα/β3 for the sigmoid colon. After a median follow-up of 15 months (range, 3 -24 months), two patients experienced local failure, and 1 showed internal iliac nodal metastasis. Despite the relatively small number of needles used, CT-guided HDRISBT for cervical cancer showed favorable DVH parameters and clinical outcome.
基金Supported by the National Natural Science Foundation of China (60372057)
文摘This paper presents an improved rate control method for H.264. First, the scene changes are detected by the average absolute difference of the brightness histograms between the adjacent frames. Then, the bit allocation and quantization parameters are adjusted, using a certain threshold. In addition, the calculation of the mean absolute difference （MAD） is modified in an alternative way, which makes the rate distortion optimization （RDO） more accurate. Extensive simulation results show that the proposed method, compared with G012, can improve the average peak signal-to-noise ratio （PSNR） and moderate the image quality.
文摘Lung Cancer is one of the hazardous diseases that have to be detected in earlier stages for providing better treatment and clinical support to patients.For lung cancer diagnosis,the computed tomography(CT)scan images are to be processed with image processing techniques and effective classification process is required for appropriate cancer diagnosis.In present scenario of medical data processing,the cancer detection process is very time consuming and exactitude.For that,this paper develops an improved model for lung cancer segmentation and classification using genetic algorithm.In the model,the input CT images are pre-processed with the filters called adaptive median filter and average filter.The filtered images are enhanced with histogram equalization and the ROI(Regions of Interest)cancer tissues are segmented using Guaranteed Convergence Particle Swarm Optimization technique.For classification of images,Probabilistic Neural Networks(PNN)based classification is used.The experimentation is carried out by simulating the model in MATLAB,with the input CT lung images LIDC-IDRI(Lung Image Database Consortium-Image Database Resource Initiative)benchmark Dataset.The results ensure that the proposed model outperforms existing methods with accurate classification results with minimal processing time.
