基于自适应最小距离分割算法和神经网络的腹水脱落癌细胞识别

THE MICROSCOPIC IMAGE SEGMENTATION AND RECOGNITION ON THE CANCER CELLS FALLEN INTO PERITONEAL EFFUSION BASED ON THE ADAPTIVE MIN-DISTANCE ALGORITHM AND NEURAL NETWORK

本文针对腹水脱落细胞显微图像的多样性、短灰度级范围、杂乱和非随机噪声等复杂特征 ,提出了自适应最小距离分割算法 ,把可疑细胞和可疑细胞核从复杂背景中分割出来。根据癌细胞的形态特征 ,给出了其 15个特征及其计算公式 ,利用这些特征构造BP神经网络分类器对腹水脱落癌细胞进行分类识别。通过对临床病例的检验分析 ,表明本算法能获得较高的诊断正确率。

Auto-segmentation of cell is one of the most interesting segmentation problems due to the complex nature of the cell tissues and to the inherent problems of video microscopic image.Objects,which are variant,narrow range of gray levels,non-random noise,are ubiquitous problems pre￣sented in this kind of images.Considering above characteristics,an adaptive min-distance algorithm is proposed in this paper,which is available to segment suspected cell and nucleus from the complex background in the microscopic image of cells fallen into peritoneal effusion.15 features of cancer cell and calculating formulas are presented respectively.These features are employed to construct a BP neural network classifier,which classifies and recognizes the cancer cells fallen into peritoneal effusion.Tests are performed using clinic cases recommended by the pathologists,results show that the proposed algorithm can efficiently segment cell image and receive higher accuracy of cancer cell diagnosis.