Text extraction is the key step in the character recognition;its accuracy highly relies on the location of the text region. In this paper, we propose a new method which can find the text location automatically to solv...Text extraction is the key step in the character recognition;its accuracy highly relies on the location of the text region. In this paper, we propose a new method which can find the text location automatically to solve some regional problems such as incomplete, false position or orientation deviation occurred in the low-contrast image text extraction. Firstly, we make some pre-processing for the original image, including color space transform, contrast-limited adaptive histogram equalization, Sobel edge detector, morphological method and eight neighborhood processing method (ENPM) etc., to provide some results to compare the different methods. Secondly, we use the connected component analysis (CCA) method to get several connected parts and non-connected parts, then use the morphology method and CCA again for the non-connected part to erode some noises, obtain another connected and non-connected parts. Thirdly, we compute the edge feature for all connected areas, combine Support Vector Machine (SVM) to classify the real text region, obtain the text location coordinates. Finally, we use the text region coordinate to extract the block including the text, then binarize, cluster and recognize all text information. At last, we calculate the precision rate and recall rate to evaluate the method for more than 200 images. The experiments show that the method we proposed is robust for low-contrast text images with the variations in font size and font color, different language, gloomy environment, etc.展开更多
目的探讨高频超声造影(CEUS)在浅表肝脏局灶性病变(FLLs)检出及诊断中的临床价值。方法选取我院浅表FLLs患者38例,共63个病变,分别使用低频凸阵探头和高频线阵探头对患者进行CEUS检查,比较低频CEUS与高频CEUS在病变可见度评分、检出率...目的探讨高频超声造影(CEUS)在浅表肝脏局灶性病变(FLLs)检出及诊断中的临床价值。方法选取我院浅表FLLs患者38例,共63个病变,分别使用低频凸阵探头和高频线阵探头对患者进行CEUS检查,比较低频CEUS与高频CEUS在病变可见度评分、检出率和良恶性鉴别诊断中的差异。结果浅表FLLs在高频CEUS下平均可见度评分和检出率分别为(3.62±0.79)分、93.7%(59/63),均显著高于低频CEUS[(2.44±1.04)分、57.1%(36/63)],差异均有统计学意义(均P<0.05)。高频CEUS对最大径≤1 cm FLLs、最大径1~2 cm FLLs和肝转移瘤的检出率均显著高于低频CEUS[96.7%(29/30)vs.46.7%(14/30)、92.6%(25/27)vs.66.7%(18/27)、100%(26/26)vs.57.7%(15/26)],差异均有统计学意义(均P<0.001)。高频CEUS鉴别诊断浅表FLLs良恶性的灵敏度、准确率分别为92.9%、91.7%,均高于低频CEUS(78.6%、77.8%),差异均有统计学意义(均P<0.05)。结论高频CEUS在提高浅表FLLs的检出率和定性诊断方面均有明显优势,具有重要的临床价值。展开更多
文摘Text extraction is the key step in the character recognition;its accuracy highly relies on the location of the text region. In this paper, we propose a new method which can find the text location automatically to solve some regional problems such as incomplete, false position or orientation deviation occurred in the low-contrast image text extraction. Firstly, we make some pre-processing for the original image, including color space transform, contrast-limited adaptive histogram equalization, Sobel edge detector, morphological method and eight neighborhood processing method (ENPM) etc., to provide some results to compare the different methods. Secondly, we use the connected component analysis (CCA) method to get several connected parts and non-connected parts, then use the morphology method and CCA again for the non-connected part to erode some noises, obtain another connected and non-connected parts. Thirdly, we compute the edge feature for all connected areas, combine Support Vector Machine (SVM) to classify the real text region, obtain the text location coordinates. Finally, we use the text region coordinate to extract the block including the text, then binarize, cluster and recognize all text information. At last, we calculate the precision rate and recall rate to evaluate the method for more than 200 images. The experiments show that the method we proposed is robust for low-contrast text images with the variations in font size and font color, different language, gloomy environment, etc.
文摘目的探讨高频超声造影(CEUS)在浅表肝脏局灶性病变(FLLs)检出及诊断中的临床价值。方法选取我院浅表FLLs患者38例,共63个病变,分别使用低频凸阵探头和高频线阵探头对患者进行CEUS检查,比较低频CEUS与高频CEUS在病变可见度评分、检出率和良恶性鉴别诊断中的差异。结果浅表FLLs在高频CEUS下平均可见度评分和检出率分别为(3.62±0.79)分、93.7%(59/63),均显著高于低频CEUS[(2.44±1.04)分、57.1%(36/63)],差异均有统计学意义(均P<0.05)。高频CEUS对最大径≤1 cm FLLs、最大径1~2 cm FLLs和肝转移瘤的检出率均显著高于低频CEUS[96.7%(29/30)vs.46.7%(14/30)、92.6%(25/27)vs.66.7%(18/27)、100%(26/26)vs.57.7%(15/26)],差异均有统计学意义(均P<0.001)。高频CEUS鉴别诊断浅表FLLs良恶性的灵敏度、准确率分别为92.9%、91.7%,均高于低频CEUS(78.6%、77.8%),差异均有统计学意义(均P<0.05)。结论高频CEUS在提高浅表FLLs的检出率和定性诊断方面均有明显优势,具有重要的临床价值。
