【摘要】：從倫琴1895年發(fā)現(xiàn)了X射線很短的時間內(nèi),X射線就得到了廣泛的應(yīng)用,其中也應(yīng)用于醫(yī)學(xué)影像。X射線的應(yīng)用促進(jìn)了醫(yī)學(xué)的發(fā)展。在近幾年,人們借助計算機(jī)可以把不同角度的X射線影像合成三維圖像,并由此產(chǎn)生了諸多診療手段,進(jìn)入了數(shù)字化、體層成像、三維仿真的重建階段。隨之而來,各種醫(yī)學(xué)影像處理技術(shù)也得到了迅速的發(fā)展。 但是由于人體組織和結(jié)構(gòu)非常復(fù)雜,再加上系統(tǒng)設(shè)備和環(huán)境等因素對X射線不利的影響,最終導(dǎo)致醫(yī)學(xué)影像質(zhì)量的下降,這主要表現(xiàn)在邊緣細(xì)節(jié)模糊,對比度差,有時還有明顯的噪聲,在很大程度上影響了醫(yī)生對疾病的診斷和治療。因此,除了應(yīng)用傳統(tǒng)的數(shù)字圖像處理技術(shù),比如直方圖處理、空間域和頻率域?yàn)V波等方法外,還應(yīng)當(dāng)嘗試新的改進(jìn)方法。Retinex理論便是其中之一,它的優(yōu)勢在于可以有效的壓縮圖像動態(tài)范圍,增強(qiáng)圖像的邊緣細(xì)節(jié),增強(qiáng)圖像的亮度,提高圖像對比度,改善圖像的視覺效果。對于醫(yī)學(xué)影像細(xì)節(jié)模糊、對比度低、分辨率低、視覺效果差的特點(diǎn),恰恰十分合適。為此,針對X射線醫(yī)學(xué)影像的特點(diǎn),論文提出了基于Retinex理論的復(fù)合LRA (Logsig cumulative Reintex Algorithm)算法,主要工作如下： 首先,分析了傳統(tǒng)的圖像增強(qiáng)方法,并研究了它們的特點(diǎn)； 其次,構(gòu)建了噪聲模型,并應(yīng)用最新的圖像降噪方法對X射線醫(yī)學(xué)影像進(jìn)行去噪處理； 最后,分析并研究了Retinex理論,并且實(shí)現(xiàn)了Retinex發(fā)展的各階段算法,利用神經(jīng)網(wǎng)絡(luò)中的對數(shù)S形LogSig傳遞函數(shù)取代原有多尺度Retinex中的對數(shù)函數(shù),并對圖像進(jìn)行動態(tài)范圍壓縮,并由此獲得更好的圖像亮度調(diào)節(jié)能力,并在此基礎(chǔ)上提出了本文復(fù)合的LRA (LogSig cumulative Retinex Algorithm)算法。通過與原有算法進(jìn)行比對分析,找到了原有Retinex算法針對X射線醫(yī)學(xué)影像應(yīng)用的不足,并說明了本文算法對于X射線醫(yī)學(xué)影像增強(qiáng)的優(yōu)勢。
[Abstract]:X-ray has been widely used in medical imaging since Roentgen discovered it in 1895. The application of X-ray has promoted the development of medicine. In recent years, with the help of computer, people can synthesize three-dimensional images from different angles of X-ray images, and many diagnostic and therapeutic methods have emerged, which have entered the stage of digitization, tomography and 3D simulation reconstruction. Subsequently, a variety of medical image processing technology has also been rapid development. However, due to the very complex tissue and structure of the human body, coupled with the adverse effects of the system, equipment and environment on the X-ray, the quality of the medical image will eventually decline. This is mainly reflected in the blurring of the edge details and the poor contrast. Sometimes there is obvious noise that greatly affects the doctor's diagnosis and treatment of the disease. Therefore, in addition to the traditional digital image processing techniques, such as histogram processing, spatial and frequency domain filtering, we should also try new and improved methods. Retinex theory is one of them. Its advantages are that it can effectively compress the dynamic range of the image, enhance the edge details of the image, enhance the brightness of the image, improve the contrast of the image, and improve the visual effect of the image. It is very suitable for medical image with fuzzy details, low contrast, low resolution and poor visual effect. Therefore, according to the characteristics of X-ray medical images, a compound LRA (Logsig cumulative Reintex Algorithm) algorithm based on Retinex theory is proposed in this paper. The main work is as follows: firstly, the traditional image enhancement methods are analyzed and their characteristics are studied. Secondly, the noise model is constructed, and the latest image de-noising method is applied to de-noising the X-ray medical image. Finally, the Retinex theory is analyzed and studied, and the algorithms of each stage of the development of Retinex are realized. The logarithmic S-shape LogSig transfer function in neural network is used to replace the logarithmic function in the original multi-scale Retinex, and the image is compressed in dynamic range. On this basis, a composite LRA (LogSig cumulative Retinex Algorithm) algorithm is proposed. By comparing with the original algorithm, the shortcomings of the original Retinex algorithm for X-ray medical image application are found, and the advantages of this algorithm for X-ray medical image enhancement are explained.
【學(xué)位授予單位】：首都師范大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2013
【分類號】：R81;TP391.41

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