癌细胞边缘检测基于迭代算法和腐蚀算法的轮廓提取毕业设计论文.doc

1、河南科技大学本科毕业设计（论文）癌细胞边缘检测（基于迭代算法和腐蚀算法的轮廓提取）摘 要提起癌症很多人都会感到恐惧，癌症是当今世界上最常见的致命疾病之一，世界上每年都有很多人死于癌症，并且发病率仍在逐年上升。癌症的治疗取决于对他早期的诊断，早期是治疗癌症的最佳时期。现在大多数癌症病例确诊的时候已经属于晚期，失去了治愈的最佳时机，因此进行准确的早期诊断和治疗已成为迫切需要解决的问题。因为癌细胞和非癌细胞对于病理专家在传统的显微镜下观察切片或涂片的方法下很难进行区分，借助现代计算机技术结合病理专家实践经验，采用图像处理技术对医学图像进行处理，可以提高判断的有效性和图像信息的使用效率，从而对癌细胞进

2、行更加准确的识别。这对医学科研与教学，以及临床诊断方面有着现实的意义和广阔的前景。数字图像处理技术已被广泛应用到生物医学领域，运用计算机对图像进行处理和分析，并进一步完成癌细胞的检测与识别，能有效的协助医生对肿瘤癌症做出诊断。在识别癌细胞时，需要做出定量的结果，人眼很难准确的完成这类工作，而利用计算机图像处理完成显微图像的分析和识别已经取得了很大的进展。近年来国内外医学图像研究者对癌细胞的检测识别提出了很多理论和方法，对癌细胞的诊断具有十分重要的意义和实践价值。细胞边缘的检测是进行细胞面积圆度个数和颜色等形态及色度学的计算和分析的基础，其检测结果直接影响病情的分析和诊断结果。经典的边缘检测算子

3、如Sobel算子，Laplacian算子等利用图像的每个像素邻域内灰度的变化来检测边缘。虽然这些算子计算简单、速度较快，但存在对噪声干扰敏感，得到孤立或分小段连续边缘像素，重叠相邻细胞边缘等缺陷，而利用最佳阀值分割和轮廓提取相结合的方法进行边缘检测，通过迭代算法求得图像分割的最佳阈值，再利用轮廓提取算法，挖去细胞内部像素点，最后剩余部分图像就是细胞的边缘，改变了传统边缘检测算法的处理顺序，通过MATLAB编程实现后，实验结果表明能有效抑制噪声干扰影响，同时能够客观地、正确地选取边缘检测的门限值，从而进行精确的细胞边缘检测。关键词：癌细胞，边缘检测，最佳阈值，轮廓提取，数字图像处理 CANCER

4、 CELL EDGE DETECTION (BASED ON ITERATIVE ALGORITHM AND CORROSION ALGORITHM, CONTOUR EXTRACTION) ABSTRACTMany people will mention cancer fear, cancer is one of the most common fatal diseases in the world today the world every year many people die of cancer, and incidence rate is still increasing ever

5、y year. The treatment of cancer depends on the diagnosis of his early, early is the best period of the treatment of cancer. The time of diagnosis of most cases of cancer are now belong to the late, lost the best time to cure, so the accurate early diagnosis and treatment has become an urgent need to

6、 address the problem.Cancer cells and non-cancer pathology experts in a traditional microscope to observe the biopsy or smear difficult to distinguish, with the help of modern computer technology, combined with practical experience of the pathologist, medical image processing using image processing

7、technology, can improve to judge the effectiveness and efficiency in the use of the image information and thus more accurate identification of cancer cells. This has practical significance and broad prospects for medical research and teaching, and clinical diagnosis.Digital image processing techniqu

8、e has been widely applied to the biomedical field, the use of computer image processing and analysis, and complete detection and recognition of cancer cells can help doctors make a diagnosis of tumor cancers. Need to be made in the identification of cancer cells, the quantitative results, the human

9、eye is difficult to accurately complete such work, and the use of computer image processing to complete the analysis and identification of the microscopic images have made great progress. In recent years, domestic and foreign medical images of cancer cells testing to identify the researchers put for

10、ward a lot of theory and method for the diagnosis of cancer cells has very important meaning and practical value.Cell edge detection is the cell area of the number of roundness and color, shape and chromaticity calculation and the basis of the analysis their test results directly affect the analysis

11、 and diagnosis of the disease. Classical edge detection operators such as Sobel operator, Laplacian operator, each pixel neighborhood of the image gray scale changes to detect the edge. Although these operators is simple, fast, but there are sensitive to noise, get isolated or in short sections of a

12、 continuous edge pixels, overlapping the adjacent cell edge defects, while the optimal threshold segmentation and contour extraction method of combining edge detection, obtained by the iterative algorithm for the optimal threshold for image segmentation, contour extraction algorithm, digging inside

13、the cell pixels, the last remaining part of the image is the edge of the cell, change the processing order of the traditional edge detection algorithm, by MATLAB programming, the experimental results that can effectively suppress the noise impact at the same time be able to objectively and correctly

14、 select the edge detection threshold, precision cell edge detection. KEY WORDS：The cancer cells, edge detection, and optimal threshold, contour extraction, digital image processing36毕业论文（设计）原创性声明本人所呈交的毕业论文（设计）是我在导师的指导下进行的研究工作及取得的研究成果。据我所知，除文中已经注明引用的内容外，本论文（设计）不包含其他个人已经发表或撰写过的研究成果。对本论文（设计）的研究做出重要贡献的个

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