Medical Image Retrieval Using SIFT Feature

Zhi, Li; Zhang, Shaomin; Zhao, Dazhe; Zhao, Hong; Lin, Shukuan

doi:10.1109/cisp.2009.5304112

Cited by 21 publications

(6 citation statements)

References 13 publications

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“…Features, in this case, are distinguishing and significative small image patches. Many feature extraction algorithms were proposed in the 1990s, such as SIFT [20] and SURF [21], which have been widely applied for object recognition [22] or medical image retrieval [23]. Following the feature extraction step, traditional machine learning classification algorithms, such as support vector machines, logistic regression or decision trees, are trained using extracted features to classify the image.…”

Section: Dr Screening Using Traditional Machine Learning Algorithmsmentioning

confidence: 99%

Diabetic Retinopathy Classification Using an Efficient Convolutional Neural Network

Gao

Leung

Miao

2019

2019 IEEE International Conference on Agents (ICA)

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Diabetic Retinopathy (DR) is a diabetic complication that affects the eyes and may lead to blurred vision or even blindness. The diagnosis of DR through retinal fundus images is traditionally performed by ophthalmologists who inspect for the presence and significance of many subtle features, a process which is cumbersome and timeconsuming. As there are many undiagnosed and untreated cases of DR, DR screening of all diabetic patients is a huge challenge. Deep convolutional neural network (CNN) has rapidly become a powerful tool for analyzing medical images. There have been previous works which use deep learning models to detect DR automatically. However, these methods employed very deep CNNs which require vast computational resources. Thus, there is a need for more computationally efficient deep learning models for automatic DR diagnosis. The primary objective of this research is to develop a robust and computationally efficient deep learning model to diagnose DR automatically. In the first part of this thesis, we propose a computationally efficient deep CNN model MobileNet-Dense which is based on the recently proposed MobileNetV2 and DenseNet models. The effectiveness of the proposed MobileNet-Dense model is demonstrated using two widely used benchmark datasets, CIFAR-10 and CIFAR-100.In the second part of the thesis, we propose an automatic DR classification system based on the ensemble of the proposed MobileNet-Dense model and the Mo-bileNetV2 model. The performance of our system is evaluated and compared with iii Abstract some of the state-of-the-art methods using two independent DR datasets, the Eye-PACS dataset and the Messidor database. On the EyePACS dataset, our system achieves a quadratic weighted kappa (QWK) score of 0.852 compared to a QWK score of 0.849 achieved by the benchmark method while using 32% fewer parameters and 73% fewer multiply-adds (MAdds). On the Messidor database, our system outperforms the state-of-the-art method on both Normal/Abnormal and Referable/Non-Referable classification tasks.iv

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Section: Dr Screening Using Traditional Machine Learning Algorithmsmentioning

confidence: 99%

Diabetic Retinopathy Classification Using an Efficient Convolutional Neural Network

Gao

Leung

Miao

2019

2019 IEEE International Conference on Agents (ICA)

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“…As an increasingly popular technique for retrieval, content-based image retrieval (CBIR) has been widely used in many real-world applications. In the medical field, CBIR also has been applied to vertebral images [2], computed tomography (CT) [3] images and different kinds of pathology images [4]. In general, CBIR extracts low-level features (color, texture, shape, etc) of the image to characterize the image content, and then measures the similarity between the query and images of the database, finally returns the k most similar images to the user interface.…”

Section: Introductionmentioning

confidence: 99%

Pathological Image Retrieval for Breast Cancer with pLSA Model

Shi

Jiang

et al. 2013

2013 Seventh International Conference on Image and Graphics

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Pathological image retrieval contributes to computer-aided diagnosis for breast cancer due to the fact that the retrieval results generally contain detailed diagnostic information (e.g. abnormal regions and diagnostic opinion from other doctors) which can offer some reference and assistance to the doctor during diagnosis process. In this paper, we present a novel pathological image retrieval approach based on probabilistic latent semantic analysis (pLSA) model. The method respectively utilizes SIFT features after visual saliency detection, and block Gabor features for the construction of two semantic codebooks, which not only can characterize the salient local invariant features and texture information under different scales and orientations in the pathological images, but also consider the high-level semantic features. Furthermore, we apply pLSA model to discover the latent topics in each codebook. Finally each pathological image is represented by the combination of topics from these two codebooks. The proposed method is evaluated on the pathological image database for breast cancer, which includes 5 categories (mucinous cystadenocarcinoma, invasive lobular carcinoma, basal-like carcinoma, invasive breast cancer and low-grade adenosquamous carcinoma) and 110 cases for each category. Experimental results demonstrate the feasibility and effectiveness of our method.

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“…15 Therefore, several SIFT-based image retrieval systems have been developed. 9,10,12,[18][19][20] However, these systems focused primarily on retrieving image of the same object or scene under di®erent conditions. There has been little related work on¯nding loosely similar image (object).…”

Section: Introductionmentioning

confidence: 99%

Integration of Keypoints and Edges for Image Retrieval

Chen

Hung

2013

Int. J. Patt. Recogn. Artif. Intell.

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The usefulness of an image database depends on whether the image of interest can be easily located. Feature extraction is a crucial step of image retrieval. The well-known SIFT descriptor is a keypoint-based image feature. It can be used to robustly¯nd the same objects in di®erent images, i.e. achieve the object recognition task. However, it is not e®ective on the image retrieval task, i.e.¯nding images with similar content. To improve the SIFT algorithm, we propose a robust image retrieval algorithm based on the integration of keypoints and edges information. Our approach is robust to translation, rotation and partial occlusion of the object. Experimental results indicate that the proposed algorithm is e®ective and outperforms the SIFT algorithm.

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Medical Image Retrieval Using SIFT Feature

Cited by 21 publications

References 13 publications

Diabetic Retinopathy Classification Using an Efficient Convolutional Neural Network

Diabetic Retinopathy Classification Using an Efficient Convolutional Neural Network

Pathological Image Retrieval for Breast Cancer with pLSA Model

Integration of Keypoints and Edges for Image Retrieval

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