Department of Electronic Engineering, Fudan University, Shanghai 200433, China.
Artif Intell Med. 2011 Mar;51(3):199-209. doi: 10.1016/j.artmed.2010.10.002. Epub 2010 Nov 10.
Polycystic ovary syndrome (PCOS) is a complex endocrine disorder that seriously affects women's health. The disorder is characterized by the formation of many follicles in the ovary. Currently the predominant diagnosis is to manually count the number of follicles, which may lead to inter-observer and intra-observer variability and low efficiency. A computer-aided PCOS diagnostic system may overcome these problems. However the methods reported in recently published literature are not very effective and often need human interaction. To overcome these problems, we propose an automated PCOS diagnostic system based on ultrasound images.
The proposed system consists of two major functional blocks: preprocessing phase and follicle identification based on object growing. In the preprocessing phase, speckle noise in the input image is removed by an adaptive morphological filter, then contours of objects are extracted using an enhanced labeled watershed algorithm, and finally the region of interest is automatically selected. The object growing algorithm for follicle identification first computes a cost map to distinguish between the ovary and its external region and assigns each object a cost function based on the cost map. The object growing algorithm initially selects several objects that are likely to be follicles with very high probabilities and dynamically update the set of possible follicles based on their cost functions. The proposed method was applied to 31 real PCOS ultrasound images obtained from patients and its performance was compared with those of three other methods, including level set method, boundary vector field (BVF) method and the fuzzy support vector machine (FSVM) classifier.
Based on the judgment of subject matter experts, the proposed diagnostic system achieved 89.4% recognition rate (RR) and 7.45% misidentification rate (MR) while the RR and MR of the level set method, the BVF method and the FSVM classifier are around 65.3% and 2.11%, 76.1% and 4.53%, and 84.0% and 16.3%, respectively. The proposed diagnostic system also achieved better performance than those reported in recently published literature.
The paper proposed an automated diagnostic system for the PCOS using ultrasound images, which consists of two major functional blocks: preprocessing phase and follicle identification based on object growing. Experimental results showed that the proposed system is very effective in follicle identification for PCOS diagnosis.
多囊卵巢综合征（PCOS）是一种严重影响女性健康的复杂内分泌疾病。该疾病的特征是卵巢中形成多个卵泡。目前主要的诊断方法是手动计数卵泡数量，这可能导致观察者间和观察者内的变异性和低效率。计算机辅助 PCOS 诊断系统可能克服这些问题。然而，最近发表的文献中报道的方法并不十分有效，并且通常需要人工交互。为了克服这些问题，我们提出了一种基于超声图像的自动化 PCOS 诊断系统。
所提出的系统由两个主要功能模块组成：预处理阶段和基于目标生长的卵泡识别。在预处理阶段，通过自适应形态滤波器去除输入图像中的斑点噪声，然后使用增强标记分水岭算法提取对象的轮廓，最后自动选择感兴趣的区域。基于目标生长的卵泡识别算法首先计算一个代价图来区分卵巢与其外部区域，并根据代价图为每个对象分配一个代价函数。目标生长算法最初选择几个具有很高概率可能是卵泡的对象，并根据它们的代价函数动态更新可能的卵泡集。该方法应用于 31 个来自患者的真实 PCOS 超声图像，并将其性能与其他三种方法（包括水平集方法、边界矢量场（BVF）方法和模糊支持向量机（FSVM）分类器）进行比较。
基于主题专家的判断，所提出的诊断系统的识别率（RR）为 89.4%，误识别率（MR）为 7.45%，而水平集方法、BVF 方法和 FSVM 分类器的 RR 和 MR 分别约为 65.3%和 2.11%、76.1%和 4.53%、84.0%和 16.3%。所提出的诊断系统的性能也优于最近发表的文献中的报道。
本文提出了一种基于超声图像的 PCOS 自动化诊断系统，该系统由两个主要功能模块组成：预处理阶段和基于目标生长的卵泡识别。实验结果表明，该系统在 PCOS 诊断中的卵泡识别非常有效。
