Retinal image registration and comparison for clinical decision support

Background

For eye diseases, such as glaucoma and age-related macular degeneration (ARMD), involved in long-term degeneration procedure, longitudinal comparison of retinal images is a common step for reliable diagnosis of these kinds of diseases.

Aims

To provide a retinal image registration approach for longitudinal retinal image alignment and comparison.

Method

Two image registration solutions were proposed for facing different image qualities of retinal images to make the registration methods more robust and feasible in a clinical application system.

Results

Thirty pairs of longitudinal retinal images were used for the registration test. The experiments showed both solutions provided good performance for the accurate image registrations with efficiency.

Conclusion

We proposed a set of retinal image registration solutions for longitudinal retinal image observation and comparison targeting a clinical application environment.     

基于医学图像降噪阈值选取的动态优化算法

SIFT算法是提取特征点的常用算法之一，具有良好的健壮性，在医学图像处理领域中广泛应用。然而传统的SIFT算法采用固定的降噪阈值会影响特征点选取的合理性，因此提出了一种动态优化算法(PM-SIFT算法)，即采用PSO算法与互信息结合的方式优化传统SIFT算法，并从特征点有效性与计算时效性两方面对2种算法的特征点提取效果进行定量分析的结果表明，与传统的SIFT算法相比，PM-SIFT算法不但可以动态地选取合适的降噪阈值，而且还能提高图像配准的效果。     

一种基于改进SURF和K-Means聚类的布料图像匹配算法

计算机图像智能处理技术为服装设计师开展设计、启发灵感提供了方便和可能。通过提取布料图像的SURF特征可以实现布料图像形状分析,但由于SURF特征维数高、特征提取是基于灰度图进行,因此存在匹配速度慢、匹配结果不够符合人眼视觉特点的问题。本文提出了基于小波变换的自适应SURF特征提取算法和基于K-Means聚类的布料图像颜色分析方法。通过融合图像形状特征、颜色特征,加快了布料图像匹配速度,使布料图像的匹配结果更加符合人眼视觉感受。在8种不同类型布料图像上的实验验证了该算法的有效性。     

Automated Diagnosis of Alzheimer Disease using the Scale-Invariant Feature Transforms in Magnetic Resonance Images

In this paper we present an automated method for diagnosing Alzheimer disease (AD) from brain MR images. The approach uses the scale-invariant feature transforms (SIFT) extracted from different slices in MR images for both healthy subjects and subjects with Alzheimer disease. These features are then clustered in a group of features which they can be used to transform a full 3-dimensional image from a subject to a histogram of these features. A feature selection strategy was used to select those bins from these histograms that contribute most in classifying the two groups. This was done by ranking the features using the Fisher’s discriminant ratio and a feature subset selection strategy using the genetic algorithm. These selected bins of the histograms are then used for the classification of healthy/patient subjects from MR images. Support vector machines with different kernels were applied to the data for the discrimination of the two groups, namely healthy subjects and patients diagnosed by AD. The results indicate that the proposed method can be used for diagnose of AD from MR images with the accuracy of %86 for the subjects aged from 60 to 80 years old and with mild AD.     

Unsupervised Fuzzy Based Vessel Segmentation In Pathological Digital Fundus Images

Performing the segmentation of vasculature in the retinal images having pathology is a challenging problem. This paper presents a novel approach for automated segmentation of the vasculature in retinal images. The approach uses the intensity information from red and green channels of the same retinal image to correct non-uniform illumination in color fundus images. Matched filtering is utilized to enhance the contrast of blood vessels against the background. The enhanced blood vessels are then segmented by employing spatially weighted fuzzy c-means clustering based thresholding which can well maintain the spatial structure of the vascular tree segments. The proposed method’s performance is evaluated on publicly available DRIVE and STARE databases of manually labeled images. On the DRIVE and STARE databases, it achieves an area under the receiver operating characteristic curve of 0.9518 and 0.9602 respectively, being superior to those presented by state-of-the-art unsupervised approaches and comparable to those obtained with the supervised methods.     

SURF1基因两个新突变致Leigh综合征1例

目的研究1例因常染色体Surfeit 1（SURF1）基因2个新突变所致Leigh综合征女童的临床及其突变特点.方法患儿为第一胎,生后运动发育落后,4岁时出现肢体震颤,进行性加重,伴运动倒退.4岁6个月来院时不能独站,左侧肢体痉挛性瘫痪,血乳酸、丙酮酸增高,腓肠肌活检未见异常.脑MRI显示双侧基底节、小脑脚上交叉多发性软化灶,符合Leigh综合征诊断.患者曾口服安坦、维生素B1等药物无效,12岁死于肺炎、呼吸衰竭.本研究运用聚合酶链式反应扩增SURF1基因的9个外显子序列,对患儿及103名正常人的外周血白细胞DNA进行正反向序列测定检测突变.结果患者线粒体基因筛查未见异常,SURF1基因测序发现了分别位于内含子3的240＋1G＞C剪切位点突变和外显子6的574C＞G错义突变两个杂合性新突变.结论细胞色素C氧化酶缺陷是Leigh综合征的常见原因,本研究通过对1例中国Leigh综合征患者的SURF1基因分析,首次发现了240＋1G＞C和574C＞G两个新突变,不仅明确了患者病因,并将进一步充实人类Leigh综合征致病基因库.     

基于密集特征匹配的胸片肺野自动分割

目的X线胸片中肺野的准确分割是胸片图像自动分析的必要步骤。本文采用局部特征的密集匹配和标号融合进行胸片
肺野的自动分割。方法对于输入的待分割胸片,基于每个像素点提取密集SIFT描述子和图像块作为局部特征,采用密集匹配
直接在整个参考图像特征集合中快速搜索近邻;密集匹配分为受限的随机初始化、近邻场传播和受限的随机搜索三步,并数次
迭代后两步。利用匹配得到的近邻场,提取标号图像块并进行加权,权重为匹配的相似度,最后重组为肺野的概率图,经阈值化
处理即可得到肺野的分割。结果在公开的JSRT胸片图像数据集上进行测试,本文方法的Jaccard指标可达95.5%。结论利用
局部特征的密集匹配和标号融合能取得准确性高且稳定的胸片肺野分割效果,并且优于当前最好的胸片肺野分割方法。
     

MSCTA血管造影对颈动脉体瘤的诊断价值

目的：探讨多排螺旋CT血管造影（MSCTA）下颈动脉体瘤的表现及其评价诊断价值。方法：搜集了病理证实的颈动脉体瘤16例，术前均行16排CT血管造影，注射速率采用4．0mL／s获得容积数据进行MIP及VR重建，总结颈动脉体瘤在MSCTA下的特点。结果：16例病变均为单发，右侧10例，左侧6例。16例MSCTA原始图像上均显著强化，CT值均在200HU以上，16例病灶内均可见粗大迂曲的血管。12例位于颈动脉分叉内后方，2例完全包绕颈内外动脉，2例完全骑跨于颈动脉分叉中使颈动脉分叉角度明显增大，其中1例破裂出血。结论：MSCTA不仅显示肿瘤大小、部位及内部特点，而且能三维立体的显示肿瘤和周围血管的关系，是诊断颈动脉体瘤的首选方法，对临床诊断及治疗具有重要的指导作用。     

基于GSO算法与互信息的医学图像配准

医学图像处理过程通常包括图像预处理、特征提取、图像分类,Harris角点检测算法是常用的特征点提取算法之一。该算法适应多种变换、运算简便,在医学图像处理领域中广泛应用,但在实际应用中发现传统的Harris算法检测到的特征点数量不足且图像配准精度不高。因此提出了一种优化算法（GM-Harris算法）,即采用群搜索优化算法（GSO算法）与互信息相结合的方式优化传统Harris算法的过程,并从匹配有效率与算法效率两方面对2种算法的特征点提取效果进行了定量分析。实验结果表明,与传统的Harris算法相比,GM-Harris算法不但可以获得较充足的特征点,而且还能提高图像配准的精度。     

Loss of bifurcation patency after cross-over stenting of ostial lesions in superficial femoral artery: possible causes, prevention and reintervention

Background Crossover stenting across the origin of the profunda femoral artery （PFA） and occasionally into the common femoral artery （CFA） is commonly used after suboptimal balloon angioplasty of ostial occlusive lesions of the superficial femoral artery （SFA） involving the bifurcation. Late stent occlusion at the bifurcation is not rare and results in severe lower extremity ischemia. Therefore, we tried to assess its possible causes, prevention and reintervention. Methods Using a prospectively maintained single-center database, 12-month femoral bifurcation patency was retrospectively compared and lesion and procedural predictors of stent occlusion were determined among 63 patients （64 lesions） who between July 2011 and February 2013 underwent crossover （36 non-jailed and 15 jailed SFA, and 12 distal and 1 complete CFA） stenting of de novo ostial SFA lesions. Results Twelve-month overall patency rate at the femoral bifurcation was 88%, with no significant difference between jailed-ostial SFA （80%） and distal CFA （67%） stenting （P=0.731）, and significant differences between either and non-jailed ostial stenting （100%,P=0.035 and 0.002）. When PFA ostium was jailed by the stent, patients with preexisting CFA or PFA lesions had a 12-month bifurcation patency rate of 20%, significantly lower than those with simple ostial SFA lesions （83%, P=0.015）. Stent induced intimal hyperplasia caused bifurcation occlusion in 6 surgical reintervention cases. Conclusions In crossover stenting of ostial lesions in SFA, bifurcation patency loss was significantly higher in distal CFA and jailed ostial SFA stenting than non-jailed ostial SFA stenting. Preexisting CFA or PFA lesion is a significant risk factor for bifurcation patency loss when PFA ostium is jailed by crossover stenting.     

Feature and Intensity Based Medical Image Registration Using Particle Swarm Optimization

Image registration is an important aspect in medical image analysis, and kinds use in a variety of medical applications. Examples include diagnosis, pre/post surgery guidance, comparing/merging/integrating images from multi-modal like Magnetic Resonance Imaging (MRI), and Computed Tomography (CT). Whether registering images across modalities for a single patient or registering across patients for a single modality, registration is an effective way to combine information from different images into a normalized frame for reference. Registered datasets can be used for providing information relating to the structure, function, and pathology of the organ or individual being imaged. In this paper a hybrid approach for medical images registration has been developed. It employs a modified Mutual Information (MI) as a similarity metric and Particle Swarm Optimization (PSO) method. Computation of mutual information is modified using a weighted linear combination of image intensity and image gradient vector flow (GVF) intensity. In this manner, statistical as well as spatial image information is included into the image registration process. Maximization of the modified mutual information is effected using the versatile Particle Swarm Optimization which is developed easily with adjusted less parameter. The developed approach has been tested and verified successfully on a number of medical image data sets that include images with missing parts, noise contamination, and/or of different modalities (CT, MRI). The registration results indicate the proposed model as accurate and effective, and show the posture contribution in inclusion of both statistical and spatial image data to the developed approach.     

Energy Spectrum CT Image Detection Based Dimensionality Reduction with Phase Congruency

The image feature detection is widely used in image registration, image stitching and object recognition. The feature detection algorithm can be applied to the detection of artificial images, and can be used to detect the energy spectrum CT image. A new algorithm of phase consistency detection based on dimensionality reduction is proposed in this paper. We mainly focus on the phase congruency of the spectral CT images in the paper and try to use dimensionality reduction to integrate the information of phase congruency detected in the image. The experimental results show that the algorithm can detect the energy spectrum CT image with clear edge and contour, which is beneficial to the subsequent processing. Meanwhile, the algorithm presented is more effective in diagnosis of disease for medical professionals.     

病理学课堂教学技能点的实证研究

目的对国家级精品课程《病理学》课堂教学录像进行观察分析与记录,探讨病理学课堂教学技能点的构成与分布特点。方法对2006—2012年国家级精品课程《病理学》课堂录像采用费兰德斯课堂分析技术,对教学教授技能、演示技能和互动技能出现的频次进行统计分析。结果病理学课堂教学技能点使用频次从高到低依次为：演示技能点（53．94％）、互动技能点（26．64％）、讲授技能点（19．42％）;其中演示技能点最常用的为感官性聚焦;互动技能点最常用的为监控、表达关怀等;讲授技能点中最常用的为手势配合、案例讲解等。结论病理学课堂教学应根据教学目标、教学内容,正确处理好演示、互动与讲授技能三者之间的关系。     

Decision Support System for Detection of Papilledema through Fundus Retinal Images

A condition in which the optic nerve inside the eye is swelled due to increased intracranial pressure is known as papilledema. The abnormalities due to papilledema such as opacification of Retinal Nerve Fiber Layer (RNFL), dilated optic disc capillaries, blurred disc margins, absence of venous pulsations, elevation of optic disc, obscuration of optic disc vessels, dilation of optic disc veins, optic disc splinter hemorrhages, cotton wool spots and hard exudates may result in complete vision loss. The ophthalmologists detect papilledema by means of an ophthalmoscope, Computed Tomography (CT), Magnetic Resonance Imaging (MRI) and ultrasound. Rapid development of computer aided diagnostic systems has revolutionized the world. There is a need to develop such type of system that automatically detects the papilledema. In this paper, an automated system is presented that detects and grades the papilledema through analysis of fundus retinal images. The proposed system extracts 23 features from which six textural features are extracted from Gray-Level Co-occurrence Matrix (GLCM), eight features from optic disc margin obscuration, three color based features and seven vascular features are extracted. A feature vector consisting of these features is used for classification of normal and papilledema images using Support Vector Machine (SVM) with Radial Basis Function (RBF) kernel. The variations in retinal blood vessels, color properties, texture deviation of optic disc and its peripapillary region, and fluctuation of obscured disc margin are effectively identified and used by the proposed system for the detection and grading of papilledema. A dataset of 160 fundus retinal images is used which is taken from publicly available STARE database and local dataset collected from Armed Forces Institute of Ophthalmology (AFIO) Pakistan. The proposed system shows an average accuracy of 92.86% for classification of papilledema and normal images. It also shows an average accuracy of 97.85% for classification of already classified papilledema images into mild and severe papilledema. The proposed system is a novel step towards automated detection and grading of papilledema. The results showed that the technique is reliable and can be used as clinical decision support system.     

Diagnosis of Diabetic Retinopathy: Automatic Extraction of Optic Disc and Exudates from Retinal Images using Marker-controlled Watershed Transformation

Due to increasing number of diabetic retinopathy cases, ophthalmologists are experiencing serious problem to automatically extract the features from the retinal images. Optic disc (OD), exudates, and cotton wool spots are the main features of fundus images which are used for diagnosing eye diseases, such as diabetic retinopathy and glaucoma. In this paper, a new algorithm for the extraction of these bright objects from fundus images based on marker-controlled watershed segmentation is presented. The proposed algorithm makes use of average filtering and contrast adjustment as preprocessing steps. The concept of the markers is used to modify the gradient before the watershed transformation is applied. The performance of the proposed algorithm is evaluated using the test images of STARE and DRIVE databases. It is shown that the proposed method can yield an average sensitivity value of about 95%, which is comparable to those obtained by the known methods.     

基于手势识别的大屏幕文字输入系统

目前基于手势交互的系统应用越来越多,但都是简单的利用手势操作鼠标进行交互,没有向系统进行文字输入的功能。利用盲人字母手势作为输入手势,采用微软的体感设备Kinect获取深度图像,对其进行手势分割,再利用SIFT提取特征,得到手势字母,利用拼音输入法,提供了一种向系统输入汉字的功能。     

制作优秀病理学教学PPT的教学应用价值及常用技巧

根据病理学在医学学科教育体系中和培养医学生内在学习驱动力过程中的重要地位和作用,提出病理学日常教学中使用优秀病理学教学PPT（ETPP）的重要应用价值和制作ETPP过程中的一些原则和常用技巧,如界面中如何突出重点、图表和文字选用要求及颜色搭配技巧和多媒体整合设计适度等。