Content-Based Image Retrieval System for Pulmonary Nodules Using Optimal Feature Sets and Class Membership-Based Retrieval

Mehre, Shrikant A.; Dhara, Ashis Kumar; Garg, Mandeep; Kalra, Naveen; Khandelwal, Niranjan; Mukhopadhyay, Sudipta

doi:10.1007/s10278-018-0136-1

Cited by 10 publications

(4 citation statements)

References 30 publications

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“…This paper clearly explained about these methods and applications. Mehre et al proposed in [11] the optimal set of feature and membership-based class Retrieval for retrieving. This paper contains the CBIR system for lung nodules with learning and optimal features to improving retrieval performance.…”

Section: Related Workmentioning

confidence: 99%

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Content Based CT Image Sign Retrieval using Fast Discrete Curvelet Transform and Deep Learning

Biswas¹,

Mahavidyalaya²

2019

IJATCSE

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In the world, one of the most critical diseases is lung cancer that leads to death of almost all affected human beings due to uncontrolled growth in the cell. These abnormal cells grow rapidly and divide to form tumor in the lungs. For lung cancer detection, the CAD system divided in four parts in CT images, such as noise removing process, segmentation of lung, detection of lung nodule and classification. The Visual information of similar nodules helps radiologists to detect the disease. This paper contains the Content-Based Image Retrieval (CBIR) system which is used for nodule retrieval in lung CT images. The CBIR system of pulmonary nodules retrieval system consists of pre-processing, feature extraction, feature selection, retrieval and classification. Our proposed method used manual cropping for segmentation phase to extract Region of Interest (ROI). The Gray-Level Co-Occurrence Matrix (GLCM) is used to extract features and the extraction of feature is done by Fast Discrete Curvelet Transform (FDCT). To select the perfect features, enhanced moth flame optimization algorithm is used and other best features are filtered by Deep Neural Networks (DNN's). We used the Euclidean distance to retrieve similar lung nodules from ROI database. The proposed method has been tested on the LISS database. Finally, we have achieved the content based image retrieval from ROI image database.

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Section: Related Workmentioning

confidence: 99%

“…It achieved to perform lung image retrieval for maximal types of shapes. Some of the shape features are calculated using equations ( 9), ( 10) and (11).…”

Section: Ii) Shape Featuresmentioning

confidence: 99%

Content Based CT Image Sign Retrieval using Fast Discrete Curvelet Transform and Deep Learning

Biswas¹,

Mahavidyalaya²

2019

IJATCSE

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“…Extraction of discriminative features from input images is one of the most challenging tasks in object recognition systems. Much effort has aimed at determining optimal feature sets for a specific task [1], based on the attributes of objects to be recognized and classifiers to be used [2] [3]. Many of these features produced very promising results.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Object Recognition Using Deep-Learning

Omar¹,

Mohammed²,

Khisro³

2021

ACAD J NAWROZ UNIV

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The great interest at the moment is focused on the field of technology, especially artificial intelligence, also do not devoid of our daily life of the use of phone applications and computer programs, that increasing of phone and computer usage demands more programs and applications to satisfy the needs of users. However, this approach starts with acquirement an image of a particular medical or engineering tool, is displayed on the computer through a webcam, whether this image is a photo or digital through a display screen, or even the device itself, the computer will identify the tool and a simplified explanation of the way it works with a video demonstration throughout MATLAB IDE for implementing this project as well as easy to use by anyone even the user doesn't have any experience software.Finally, this approach has been created this project to save time and effort for the users instead of searching on a specific tool that they need about its name, how to use so we tried to facility this matter. The proposed algorithm got accurate result, for the doctor's tools the accuracy was 95.8 %, for the engineer's tools was 98.3 % and for mix of them was approximately 94.1 %.

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“…The shorter distances corresponded to higher similarity. To obtain a good performance, they chose the favorable metric according to the descriptors, like the city-block distance [7], the Mahalanobis distance [8], Manhattan distance [9], cosine distance [10], and Euclidian distance [11]. The other methods measured the semantic-level similarity based on the classification information.…”

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confidence: 99%

A multi-level similarity measure for the retrieval of the common CT imaging signs of lung diseases

Liu

Fei

2020

Med Biol Eng Comput

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The common CT imaging signs of lung diseases (CISLs) which frequently appear in lung CT images are widely used in the diagnosis of lung diseases. Computer-aided diagnosis (CAD) based on the CISLs can improve radiologists' performance in the diagnosis of lung diseases. Since similarity measure is important for CAD, we propose a multi-level method to measure the similarity between the CISLs. The CISLs are characterized in the low-level visual scale, mid-level attribute scale, and high-level semantic scale, for a rich representation. The similarity at multiple levels is calculated and combined in a weighted sum form as the final similarity. The proposed multi-level similarity method is capable of computing the level-specific similarity and optimal cross-level complementary similarity. The effectiveness of the proposed similarity measure method is evaluated on a dataset of 511 lung CT images from clinical patients for CISLs retrieval. It can achieve about 80% precision and take only 3.6 ms for the retrieval process. The extensive comparative evaluations on the same datasets are conducted to validate the advantages on retrieval performance of our multi-level similarity measure over the single-level measure and the two-level similarity methods. The proposed method can have wide applications in radiology and decision support.

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Content-Based Image Retrieval System for Pulmonary Nodules Using Optimal Feature Sets and Class Membership-Based Retrieval

Cited by 10 publications

References 30 publications

Content Based CT Image Sign Retrieval using Fast Discrete Curvelet Transform and Deep Learning

Content Based CT Image Sign Retrieval using Fast Discrete Curvelet Transform and Deep Learning

Real-Time Object Recognition Using Deep-Learning

A multi-level similarity measure for the retrieval of the common CT imaging signs of lung diseases

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