Multiwavelet grading of pathological images of prostate
Multiwavelet grading of pathological images of prostate
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DOI：
10.1109/tbme.2003.812194
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发表时间：
2003-06-01
影响因子：
4.6
通讯作者：
Soltanian-Zadeh, H
中科院分区：
文献类型：
--
作者：
Jafari-Khouzani, K;Soltanian-Zadeh, H
病理图像的组织学分级用于确定癌组织的恶性程度。这是前列腺癌预后的一个非常重要的任务，因为它是用于治疗计划的。如果磁共振成像、计算机断层扫描和超声波等非侵入性诊断技术没有排除癌症感染，那么就对组织的活组织样本进行测试。对于前列腺，活检组织用苏木精-伊红染色，病理医生在显微镜下观察以确定其组织学分级。由于观察者间和观察者内的差异，人类评分是非常主观的，在某些情况下是困难和耗时的。因此，需要一种自动化和可重复的技术来进行评分。Gleason分级系统是目前最常用的前列腺癌组织学分级方法。根据这一系统，每个癌症标本被分配到五个等级中的一个。虽然已经开发了一些用于病理图像分析的自动化系统，但格里森分级尚未实现自动化；本研究的目标是使其自动化。为此，我们计算了的多小波系数的能量和熵特征。这个形象。然后，通过模拟退火法选择最具区分性的特征，并使用k近邻分类器将每幅图像分类到合适的等级(类)。采用留一法进行误码率估计。我们还利用小波包和共生矩阵提取的特征得到了结果，并与多小波方法进行了比较。实验结果表明，与其他方法相比，多小波变换具有一定的优越性。对于多小波，临界采样预处理法优于重复行预处理法，且对第二级分解的噪声敏感性较低。第一级分解对噪声非常敏感，因此不应用于特征提取。最佳多小波方法对前列腺病理图像的分类正确率为97%。
Histological grading of pathological images is used to determine level of malignancy of cancerous; tissues. This is a very important task in prostate cancer prognosis, since it is, used for treatment planning. If infection of cancer is not rejected by non-invasive diagnostic techniques like magnetic resonance imaging, computed tomography scan, and ultrasound, then biopsy specimens of tissue are tested. For prostate, biopsied tissue is stained by hematoxyline and eosine method and viewed by pathologists under a microscope to determine its histological grade. Human grading is very subjective due to interobserver and intraobserver variations and in some cases difficult and time-consuming. Thus, an automatic and repeatable technique is needed for grading. Gleason grading system is the most common method for histological grading of prostate tissue samples. According to this system, each cancerous specimen is assigned one of five grades. Although some automatic systems have been developed for analysis, of pathological images, Gleason grading has not yet been automated; the goal of this research is to automate it. To this end, we calculate energy and entropy features of multiwavelet coefficients of. the image. Then, we select most discriminative features by simulated annealing and use a k-nearest neighbor classifier to classify each image to appropriate grade (class). The leaving-one-out technique is used for error rate estimation. We also obtain the results using features extracted by wavelet packets and co-occurrence matrices and compare them with the multiwavelet method. Experimental results show the superiority of the multiwavelet transforms compared with other techniques. For multiwavelets, critically sampled preprocessing outperforms repeated-row preprocessing and has less sensitivity to noise for second level of decomposition. The first level of decomposition is very sensitive to noise and, thus, should not be used for feature extraction. The best multiwavelet method grades prostate pathological images correctly 97% of the time.