本文選題：計算機輔助診斷　切入點：肝臟B超圖像　出處：《青島大學》2017年碩士論文　論文類型：學位論文

【摘要】：本文致力于計算機輔助診斷系統(tǒng)(Computer Aided Diagnosis,簡稱CAD)中肝臟B超圖像的識別研究,目標是建立一個可協(xié)助臨床醫(yī)生識別多類肝臟B超圖像的計算機輔助診斷平臺,完成對正常肝、肝硬化、脂肪肝三類B超圖像的識別。整個計算機輔助診斷系統(tǒng)主要由感興趣區(qū)域(Region of Interest,ROI)的選取、圖像預處理、特征提取和選擇以及分類器識別這四個模塊組成。首先在有經驗的醫(yī)生的指導下劃出肝臟區(qū)域,在指定范圍內提取感興趣區(qū)域,然后對獲得的ROI進行預處理,包括圖像去噪和圖像增強,接下來對ROI進行特征提取和特征選擇得到最優(yōu)特征子集,最后將特征集合輸入設計好的分類器進行分類識別。文章主要研究了肝臟B超圖像紋理特征提取和選擇算法以及分類器的設計問題。由于肝臟病變形態(tài)的多樣性,并且沒有一定的規(guī)律可循,對于紋理特征的獲得,本文從灰度、空間、頻率三個方面進行考慮,涉及到一階灰度統(tǒng)計、灰度共生矩陣、灰度差直方圖、小波包變換。利用所提取的紋理特征就可以通過分類器對三種肝臟B超圖像(正常、脂肪肝、肝硬化)進行分類識別。但是其中有些特征會包含重復信息,對分類效果產生負面影響,所以在特征提取之后需要設計一個特征選擇模塊,本文引入遺傳算法從原始特征集合中選出一個可分性好的最優(yōu)特征子集,以消除特征冗余。最后將最優(yōu)特征子集作為分類器的輸入。關于分類器的設計,本文在傳統(tǒng)神經網絡的基礎上引入Adaboost理論,構建出一種分類效果較傳統(tǒng)分類器更好的強分類器,依據最后的分類結果對CAD的性能做出評價,最終完成肝臟B超圖像的識別研究。
[Abstract]:This paper is devoted to the research on the recognition of B-ultrasound images of liver in computer Aided diagnosis system (CAD). The aim is to establish a computer-aided diagnosis platform to assist clinicians in identifying various kinds of B-ultrasound images of liver, and to complete the diagnosis of normal liver. Recognition of three types of B-ultrasound images of liver cirrhosis and fatty liver. The whole computer-aided diagnosis system is mainly selected by region of interest region of interest and image preprocessing. The four modules of feature extraction and selection and classifier recognition are composed of four modules. Firstly, the liver region is drawn under the guidance of experienced doctors, and the region of interest is extracted within the specified range, and then the obtained ROI is preprocessed. Image denoising and image enhancement are included, and then the optimal feature subset is obtained by feature extraction and feature selection for ROI. Finally, the feature set is input into the designed classifier for classification and recognition. This paper mainly studies the texture feature extraction and selection algorithm of liver B-ultrasound image and the design of the classifier. And there are no certain rules to follow, for texture features, this paper from gray, space, frequency three aspects to consider, involving the first order gray statistics, gray level co-occurrence matrix, gray difference histogram, Wavelet packet transform. Using the extracted texture features, we can classify three kinds of liver B-ultrasound images (normal, fatty liver, liver cirrhosis) by classifier. But some of the features will contain repeated information. It is necessary to design a feature selection module after feature extraction. In this paper, genetic algorithm is introduced to select an optimal feature subset with good separability from the original feature set. Finally, the optimal feature subset is used as the input of the classifier. On the design of the classifier, Adaboost theory is introduced based on the traditional neural network to construct a strong classifier with better classification effect than the traditional classifier. According to the final classification results, the performance of CAD was evaluated, and the recognition of liver B-ultrasound image was finally completed.
【學位授予單位】：青島大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：R575;TP391.41

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相關期刊論文 前10條

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