本文選題：醫(yī)學(xué)X光圖像 + 感興趣區(qū)域�。� 參考：《西安工業(yè)大學(xué)》2017年碩士論文

【摘要】：隨著計(jì)算機(jī)輔助醫(yī)療技術(shù)的發(fā)展,醫(yī)學(xué)X光圖像分析逐漸成為生物醫(yī)學(xué)領(lǐng)域的研究熱點(diǎn)。椎體部分作為腰椎X光圖像的感興趣區(qū)域,從腰椎圖像中提取椎體的信息對(duì)于腰椎疾病的診斷具有參考價(jià)值。但是受原始X光圖像中存在的對(duì)比度低、邊緣模糊與噪音污染等干擾因素影響,腰椎圖像中椎體信息的分析面臨諸多挑戰(zhàn)。論文針對(duì)腰椎X光圖像中感興趣區(qū)域的提取進(jìn)行研究,從X光圖像特性與醫(yī)學(xué)圖像處理出發(fā),對(duì)圖像處理算法在醫(yī)學(xué)X光圖像處理方面的不足進(jìn)行了深入的分析,提出了以去霧霾算法思想改進(jìn)的反銳化掩膜增強(qiáng)方法、基于相位一致性的綜合邊緣檢測(cè)方法和基于自定義廣度霍夫變換的醫(yī)學(xué)X光圖像感興趣區(qū)域提取方法,實(shí)現(xiàn)了對(duì)腰椎圖像中感興趣區(qū)域的有效提取。論文的主要工作如下:第一,對(duì)X光像的成像原理進(jìn)行了深入的分析。在對(duì)X光圖像充分了解的基礎(chǔ)上對(duì)醫(yī)學(xué)X光圖像進(jìn)行增強(qiáng)處理,對(duì)空間域與頻率域的圖像增強(qiáng)方法進(jìn)行了學(xué)習(xí)實(shí)驗(yàn),對(duì)空間域中的直方圖均衡化,限定對(duì)比度自適應(yīng)直方圖均衡化、平滑濾波、銳化濾波、灰度變換等算法,以及頻率域中的傅里葉變換、高通、低通濾波算法進(jìn)行了研究,在此基礎(chǔ)上根據(jù)霧霾圖像與醫(yī)學(xué)X光圖像擁有的特性,引入了去霧理論的引導(dǎo)圖濾波方法,設(shè)計(jì)實(shí)現(xiàn)了圖像預(yù)處理的框架,良好地保持了圖像邊緣特性。第二,對(duì)圖像的邊緣提取算法進(jìn)行了研究。通過實(shí)驗(yàn)認(rèn)真研究了Sobel算子、Roberts算子、Prewitt算子、Log算子和Canny算子,在此基礎(chǔ)上實(shí)現(xiàn)了基于相位一致性的綜合邊緣檢測(cè)方法,作為自定義廣義霍夫變換的邊緣提取方案。第三,對(duì)感興趣區(qū)域進(jìn)行了判定與劃分。針對(duì)感興趣區(qū)域提取進(jìn)行研究,實(shí)現(xiàn)了自定義廣義霍夫變換對(duì)X光醫(yī)學(xué)圖像感興趣區(qū)域提取,廣義霍夫變換成功的關(guān)鍵在于使用相匹配的邊緣信息進(jìn)行分割。對(duì)大量的X光腰椎圖像進(jìn)行實(shí)驗(yàn),實(shí)現(xiàn)有效的分割。論文將霧霾圖像與醫(yī)學(xué)X光圖像處理技術(shù)相結(jié)合,在對(duì)醫(yī)學(xué)X光圖像進(jìn)行增強(qiáng)的同時(shí),保持了圖像的邊緣細(xì)節(jié)信息。對(duì)提取到的邊緣進(jìn)行區(qū)域生長(zhǎng),能夠顯示出整個(gè)椎體并且最大程度地減少噪聲。自定義腰椎模板可以有效實(shí)現(xiàn)對(duì)腰椎X光圖像的感興趣區(qū)域進(jìn)行粗粒度與細(xì)粒度的提取,為醫(yī)生對(duì)于病情的診斷提供更加可靠的依據(jù)。
[Abstract]:With the development of computer aided medical technology, medical X optical image analysis has gradually become a research hotspot in the field of biomedicine. The vertebral part is a region of interest in the X light image of the lumbar spine. The information extracted from the lumbar image is of reference value for the diagnosis of lumbar disease. But the contrast of the original X light image is low. The analysis of the vertebral information in the lumbar image faces many challenges. The thesis focuses on the extraction of the region of interest in the X light image of the lumbar spine. From the characteristics of the X optical image and the medical image processing, the deficiency of the image processing algorithm in the medical X optical image processing is deeply studied. In this paper, an improved method of anti sharpening mask enhancement based on the idea of haze removal is proposed. A comprehensive edge detection method based on phase consistency and an area of interest region extraction based on medical X optical image based on the custom breadth Hof transform are used to achieve the effective extraction of the region of interest in the lumbar image. The main work of this paper is as follows: First, the imaging principle of X optical image is deeply analyzed. Based on the full understanding of the X optical image, the medical X optical image is enhanced and the image enhancement method in the space domain and frequency domain is studied. The histogram equalization in the spatial domain is balanced, the adaptive histogram equalization, smoothing filtering and sharpening are limited. Filtering, gray transformation and other algorithms, as well as Fourier transform, high pass and low pass filtering algorithms in frequency domain are studied. Based on the characteristics of haze images and medical X optical images, a bootstrap filter method of fog theory is introduced. The frame of image preprocessing is designed and realized, and the edge characteristics of the image are kept well. Second, the edge extraction algorithm of image is studied. Through the experiment, Sobel operator, Roberts operator, Prewitt operator, Log operator and Canny operator are carefully studied. On this basis, a comprehensive edge detection method based on phase consistency is implemented as the edge extraction scheme of the custom generalized Hof transform. Third, region of interest to the region of interest. According to the study of the region of interest extraction, a custom generalized Hof transform (user-defined generalized Hof transform) is used to extract the region of interest in the medical image. The key to the success of the generalized Hof transform is to use the matched edge information to divide the image. Experiments on a large number of X optical lumbar images are carried out to achieve effective segmentation. The haze image is combined with the medical X optical image processing technology to enhance the image edge details while enhancing the medical X light image. The region grows on the extracted edge, can display the whole vertebral body and reduce the noise to the maximum extent. The custom lumbar template can effectively realize the feeling of the X light image of the lumbar spine. The interesting area is coarse grained and fine-grained extraction, which provides a more reliable basis for doctors to diagnose the disease.

【學(xué)位授予單位】：西安工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：R814;TP391.41

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