本文選題：醫(yī)療圖像分割　切入點(diǎn)：GRF　出處：《昆明理工大學(xué)》2017年碩士論文

【摘要】：醫(yī)學(xué)圖像分割在臨床醫(yī)學(xué)的研究和實(shí)際應(yīng)用中具有重要的作用。借助于醫(yī)療圖像分割,使得臨床醫(yī)生對(duì)疾病部位能夠更直接、更清楚、更便捷地進(jìn)行診療。由于醫(yī)療圖像受到噪聲污染、算法、設(shè)備、顯示技術(shù)等因素的影響,使得醫(yī)療影像具有模糊性等。為了解決這些醫(yī)療影像處理中的圖像分割精度、分割效率等問題,采用MRF理論、GRF理論、知識(shí)、模糊理論、D-S理論等多種融合算法進(jìn)行三維圖像分割,實(shí)現(xiàn)醫(yī)療圖像分割的最佳效果。醫(yī)療圖像分割是對(duì)圖像的提取、分割、復(fù)原、重構(gòu)、融合、識(shí)別等的過程。綜合利用多次成像或多種成像設(shè)備的圖形信息,補(bǔ)償數(shù)據(jù)丟失、局部數(shù)據(jù)不精確以及不明確造成的局限性,使用MRF模型使圖像的分割更精確。分析在腦部NMRI圖片等分割中的具體應(yīng)用效果。采用模糊聚類分割算法,使用灰度圖信息和加入空間圖像信息,無手動(dòng)設(shè)置參數(shù),實(shí)現(xiàn)了 NDFCM法比FCM法更好的圖像分割效果,具有更強(qiáng)的抗噪性能和極高的醫(yī)療價(jià)值。利用最新FCM進(jìn)行分類的MRF復(fù)原來獲取更高精度的腦核組織圖片,再用Markov和模糊聚類分別進(jìn)行腦部圖像分割并把這個(gè)作為D-S證據(jù)理論基本概率統(tǒng)計(jì)的條件,最后用D-S理論對(duì)腦部組織融合與分割,提高了醫(yī)療圖形分割速度和質(zhì)量,實(shí)現(xiàn)對(duì)腦部組織進(jìn)行定性定量地分析研究。采用最新的圖像模糊C均值(FCM)法,對(duì)醫(yī)療圖形進(jìn)行更精確分割。創(chuàng)新了高效的二維的間距程度測(cè)量技術(shù),用相應(yīng)的二維的直方圖來定義鄰域相關(guān)性。給出聚類中心v同一時(shí)間在像素值和鄰域像素值二維的坐標(biāo)上刷新的觀點(diǎn),實(shí)現(xiàn)了用鄰域空間圖像信息的二維FCM分割算法。采用D-S理論在融合兩個(gè)或多個(gè)圖像信息時(shí),利用現(xiàn)有的的多種先驗(yàn)摸型進(jìn)行融合,解決數(shù)據(jù)丟失、部分?jǐn)?shù)據(jù)不精確、模糊或不明確因素造成的缺點(diǎn)。根據(jù)圖像模糊聚類C-均值(FCM)理論和MRF理論,由條件概率服從高斯信號(hào)分布及先驗(yàn)概率,設(shè)置概率值M,采用D-S理論進(jìn)行多種數(shù)據(jù)融合,利用D-S決策準(zhǔn)則進(jìn)行決策劃分,提高了圖像的分割精確。醫(yī)療圖像分割方法主要以智能化、高精度、復(fù)雜度、抗噪能力、高速度、魯棒能力、自適應(yīng)能力等多個(gè)方面作為研究對(duì)象。傳統(tǒng)的分割技術(shù)與最新的先進(jìn)分割技術(shù)相融合是未來醫(yī)療圖像分割技術(shù)的發(fā)展趨勢(shì)。
[Abstract]:Medical image segmentation plays an important role in the research and practical application of clinical medicine. Because of the influence of noise pollution, algorithm, equipment, display technology and other factors, medical images have fuzziness. In order to solve the segmentation accuracy of these medical images, In order to realize the best effect of medical image segmentation, MRF theory, knowledge, fuzzy theory and D-S theory are used to realize the best effect of medical image segmentation. The process of reconstruction, fusion, recognition, etc., using the graphical information of multiple imaging or multiple imaging devices to compensate for the limitations caused by data loss, local data imprecision, and uncertainty. The MRF model is used to make the image segmentation more accurate. The concrete application effect in the brain NMRI image segmentation is analyzed. The fuzzy clustering segmentation algorithm is adopted, the gray image information and the spatial image information are added, and the parameters are not set manually. The image segmentation effect of NDFCM method is better than that of FCM method, and it has stronger anti-noise performance and higher medical value. The new FCM is used to restore the classified MRF to obtain more accurate brain tissue images. Then Markov and fuzzy clustering are used to segment the brain image, which is regarded as the basic probability and statistics condition of D-S evidence theory. Finally, the D-S theory is used to fuse and segment the brain tissue, which improves the speed and quality of medical image segmentation. To achieve qualitative and quantitative analysis of brain tissue. The latest image fuzzy C-means (FCM) method is used to segment medical images more accurately. The neighborhood correlation is defined by the corresponding two-dimensional histogram, and the view that the cluster center v refreshes at the same time on the coordinates of the pixel value and the neighborhood pixel value is given. A two-dimensional FCM segmentation algorithm using neighborhood spatial image information is implemented. When two or more image information is fused by D-S theory, existing prior models are used to solve the problem of data loss and partial data imprecision. According to the theory of image fuzzy clustering C-means FCM) and MRF theory, according to the conditional probability from Gao Si signal distribution and a priori probability, the probability value M is set, and the D-S theory is used to perform a variety of data fusion. The D-S decision criterion is used to divide the image to improve the accuracy of image segmentation. The medical image segmentation methods are mainly intelligent, high precision, complexity, anti-noise, high speed, robust ability, the main methods of medical image segmentation are intelligent, high precision, complexity, anti-noise ability, high speed, robust ability. The combination of traditional segmentation technology and the latest advanced segmentation technology is the development trend of medical image segmentation technology in the future.
【學(xué)位授予單位】：昆明理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：R310;TP391.41

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