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  本文選題：圖像去噪　切入點：圖像增強　出處：《廣西師范大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：宮頸癌日益威脅著廣大女性的健康,因而宮頸癌的早期篩查預(yù)防就顯得非常必要,計算機輔助自動化診斷可以有效減少人工對宮頸細胞圖像的判讀的誤差,并降低人工成本,使宮頸癌篩查技術(shù)可以快速推廣,具有很好的社會價值和經(jīng)濟效益。本文對宮頸細胞圖像分類識別方法的關(guān)鍵技術(shù)進行了研究,包括宮頸細胞圖像去噪,增強,特征提取和分類識別。主要研究內(nèi)容為:(1)采用基于塊組的非局部自相似先驗學(xué)習(xí)圖像去噪(patch group based nonlocal self-similarity prior learning for image denoising,PGPD)方法用于宮頸細胞圖像去噪處理。仿真實驗表明本文所采用的去噪方法對宮頸細胞圖像去噪的同時能夠較好地保護宮頸細胞圖像的結(jié)構(gòu)信息,且在噪聲增加時峰值信噪比(peak signal to noise ratio,PSNR)與結(jié)構(gòu)相似性指數(shù)(structural similarity index,SSIM)降低的程度較小,因而具有較好的魯棒性。(2)采用基于自適應(yīng)S型函數(shù)的B直方圖均衡方法對宮頸細胞圖像進行增強處理,使得圖像特征突出,有利于特征提取。(3)在PCANet的基礎(chǔ)上構(gòu)造聯(lián)合特征PCANet將網(wǎng)絡(luò)中間層提取的特征與最后一層輸出的特征聯(lián)合起來作為最終的特征輸出,聯(lián)合特征PCANet可以減少圖像特征在逐層提取過程中的丟失,因而使最后提取的特征能更好地表征圖像之間的差異。得到提取的特征后再利用SVM進行分類識別。仿真實驗表明本文方法對宮頸細胞圖像二分類識別準確率為95.71%,三分類識別準確率為85.40%,具有一定應(yīng)用價值。(4)基于MATLAB GUI設(shè)計了宮頸細胞圖像分類識別系統(tǒng),包含訓(xùn)練和檢測兩個模塊,實現(xiàn)聯(lián)合特征PCANet網(wǎng)絡(luò)和分類器的訓(xùn)練以及宮頸細胞圖像的檢測,功能實現(xiàn)完整,操作簡潔,具有較好的應(yīng)用價值。
[Abstract]:Cervical cancer is increasingly threatening the health of women, so the early screening and prevention of cervical cancer is very necessary. Computer aided automated diagnosis can effectively reduce the error of artificial interpretation of cervical cell images, and reduce the cost of labor. So that cervical cancer screening technology can be quickly popularized, with good social value and economic benefits. This paper studies the key technology of cervical cell image classification and recognition, including cervical cell image denoising, enhancement, Feature extraction and classification recognition. The main content of the study is: 1) using the non-local self-similar priori learning image denoising patch group nonlocal self-similarity prior learning for image DenoisingPGPDs method for cervical cell image denoising. The simulation results show that the proposed method can be used in cervical cell image denoising. The method used in this paper can protect the structure information of cervical cell image while removing noise from cervical cell image. The decrease of peak signal to noise PSNRs and structural similarity index (SSIMI) was smaller when noise increased. Therefore, the method of B-histogram equalization based on adaptive S-function is used to enhance the cervical cell image, which makes the image feature prominent. It is advantageous to construct a joint feature based on PCANet. PCANet combines the features extracted from the middle layer of the network with the features of the last layer as the final feature output. Joint feature PCANet can reduce the loss of image features in the process of layer by layer extraction. As a result, the extracted features can better represent the differences between the images. The extracted features are then classified and recognized by SVM. The simulation results show that the accuracy of the method is 95.71 for cervical cell images. The accuracy of three classification recognition is 85.40, which has certain application value. (based on MATLAB GUI, a cervical cell image classification and recognition system is designed. It includes two modules: training and detecting, which realizes the training of joint feature PCANet network and classifier and the detection of cervical cell image. The function is complete, the operation is simple, and it has good application value.
【學(xué)位授予單位】：廣西師范大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：R737.33;TP391.41

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本文編號：1602389