Learning lung nodule similarity using a genetic algorithm

Seitz, Kerry A.; Giuca, Anne-Marie; Furst, Jacob; Raicu, Daniela

doi:10.1117/12.911435

Cited by 5 publications

(2 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…SEITZ et al [ 15 ] determined an ideal vector representation from a set of 63 attributes extracted from the texture, shape, size and intensity. To select the best attributes, they used genetic algorithms to find the best combination of attributes.…”

Section: Resultsmentioning

confidence: 99%

“…SEITZ et al [ 15 ] describes a CBIR system in combination with genetic algorithms to determine the optimal combination of image attributes to increase the accuracy in retrieval of similar nodules. Sixty three attributes were extracted from texture (using Gabor filter, Markov Random Fields and attributes proposed by Haralick from Coocurrence Matrix (COM)), size, shape and intensity to represent vectorially nodules.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Automatic weighing attribute to retrieve similar lung cancer nodules

Lucena

Ferreira

Machado

et al. 2016

BMC Med Inform Decis Mak

View full text Add to dashboard Cite

BackgroundCancer is a disease characterized as an uncontrolled growth of abnormal cells that invades neighboring tissues and destroys them. Lung cancer is the primary cause of cancer-related deaths in the world, and it diagnosis is a complex task for specialists and it presents some big challenges as medical image interpretation process, pulmonary nodule detection and classification. In order to aid specialists in the early diagnosis of lung cancer, computer assistance must be integrated in the imaging interpretation and pulmonary nodule classification processes. Methods of Content-Based Image Retrieval (CBIR) have been described as one promising technique to computer-aided diagnosis and is expected to aid radiologists on image interpretation with a second opinion. However, CBIR presents some limitations: image feature extraction process and appropriate similarity measure. The efficiency of CBIR systems depends on calculating image features that may be relevant to the case similarity analysis. When specialists classify a nodule, they are supported by information from exams, images, etc. But each information has more or less weight over decision making about nodule malignancy. Thus, finding a way to measure the weight allows improvement of image retrieval process through the assignment of higher weights to that attributes that best characterize the nodules.MethodsIn this context, the aim of this work is to present a method to automatically calculate attribute weights based on local learning to reflect the interpretation on image retrieval process. The process consists of two stages that are performed sequentially and cyclically: Evaluation Stage and Training Stage. At each iteration the weights are adjusted according to retrieved nodules. After some iterations, it is possible reach a set of attribute weights that optimize the recovery of similar nodes.ResultsThe results achieved by updated weights were promising because was possible increase precision by 10% to 6% on average to retrieve of benign and malignant nodules, respectively, with recall of 25% compared with tests without weights associated to attributes in similarity metric. The best result, we reaching values over 100% of precision average until thirtieth lung cancer nodule retrieved.ConclusionsBased on the results, WED applied to the three vectors used attributes (3D TA, 3D MSA and InV), with weights adjusted by the process, always achieved better results than those found with ED. With the weights, the Precision was increased on average by 17.3% compared with using ED.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Automatic weighing attribute to retrieve similar lung cancer nodules

Lucena

Ferreira

Machado

et al. 2016

BMC Med Inform Decis Mak

View full text Add to dashboard Cite

show abstract

Content-Based Image Retrieval System for Pulmonary Nodules: Assisting Radiologists in Self-Learning and Diagnosis of Lung Cancer

et al. 2016

View full text Add to dashboard Cite

Visual information of similar nodules could assist the budding radiologists in self-learning. This paper presents a content-based image retrieval (CBIR) system for pulmonary nodules, observed in lung CT images. The reported CBIR systems of pulmonary nodules cannot be put into practice as radiologists need to draw the boundary of nodules during query formation and feature database creation. In the proposed retrieval system, the pulmonary nodules are segmented using a semi-automated technique, which requires a seed point on the nodule from the end-user. The involvement of radiologists in feature database creation is also reduced, as only a seed point is expected from radiologists instead of manual delineation of the boundary of the nodules. The performance of the retrieval system depends on the accuracy of the segmentation technique. Several 3D features are explored to improve the performance of the proposed retrieval system. A set of relevant shape and texture features are considered for efficient representation of the nodules in the feature space. The proposed CBIR system is evaluated for three configurations such as configuration-1 (composite rank of malignancy "1","2" as benign and "4","5" as malignant), configuration-2 (composite rank of malignancy "1","2", "3" as benign and "4","5" as malignant), and configuration-3 (composite rank of malignancy "1","2" as benign and "3","4","5" as malignant). Considering top 5 retrieved nodules and Euclidean distance metric, the precision achieved by the proposed method for configuration-1, configuration-2, and configuration-3 are 82.14, 75.91, and 74.27 %, respectively. The performance of the proposed CBIR system is close to the most recent technique, which is dependent on radiologists for manual segmentation of nodules. A computer-aided diagnosis (CAD) system is also developed based on CBIR paradigm. Performance of the proposed CBIR-based CAD system is close to performance of the CAD system using support vector machine.

show abstract

Innovative detection of lung cancer using decision tree classifier and comparison with K-nearest neighbor classifier

Kumar,

Jagadeesh

2024

Fifth International Conference on Applied Sciences: Icas2023

View full text Add to dashboard Cite

Learning lung nodule similarity using a genetic algorithm

Cited by 5 publications

References 19 publications

Automatic weighing attribute to retrieve similar lung cancer nodules

Automatic weighing attribute to retrieve similar lung cancer nodules

Content-Based Image Retrieval System for Pulmonary Nodules: Assisting Radiologists in Self-Learning and Diagnosis of Lung Cancer

Innovative detection of lung cancer using decision tree classifier and comparison with K-nearest neighbor classifier

Contact Info

Product

Resources

About