A Novel Method for Lung Image Processing Using Complex Networks

Broască, Laura; Trușculescu, Ana Adriana; Ancusa, V.; Ciocârlie, Horia; Oancea, Cristian; Stoicescu, Emil Robert; Manolescu, Diana

doi:10.20944/preprints202207.0156.v1

Cited by 2 publications

(4 citation statements)

References 25 publications

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“…It is not an absolute value, its meaning is related to the swiftness of change, therefore highlighting rapidly deteriorating areas. Since the algorithm behind the CN conversion considers lesions as small as 3 mm [26], by default, the speed is more granular than human eye.…”

Section: Discussionmentioning

confidence: 99%

“…A mixture of pattern matching and analyzing techniques, the complex network approach [26] might provide insights previously unexplored by the other CADs.…”

Section: Computer Aided Diagnosismentioning

confidence: 99%

“…The current paper explores the practical use of a complex networks (CN) analytical approach based on [26] and its suitability to provide early discovery and/or support/enhance clinical diagnosis by offering a reliable quantifiable progression metric.…”

Section: Hypothesis To Be Exploredmentioning

confidence: 99%

“…The transformation of DICOM images by the studied algorithm [26] takes place in multiple stages: First DICOM images are analyzed and each pixel is converted into its HU unit equivalent. Then, depending on the desired HU bands, only pixels pertaining to said bands are kept, eliminating all other.…”

Section: Computer-enhancing the Datamentioning

confidence: 99%

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Enhancing Imagistic Interstitial Lung Disease Diagnosis by Using Complex Networks

Trușculescu¹,

Manolescu²,

Broască³

et al. 2022

Preprint

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Diffuse interstitial lung diseases (DILD) are a heterogeneous group of over 200 entities, some with dramatical evolution and poor prognostic. Because of their overlapping clinical, physiopathological and imagistic nature, successful management requires early detection and proper progression evaluation. This paper tests a complex networks (CN) algorithm for imagistic aided diagnosis fitness for the possibility of achieving relevant and novel DILD management data. 65 DILD and 31 normal high resolution computer tomography (HRCT) scans were selected and analyzed with the CN model. The algorithm is showcased in two case reports and then statistical analysis on the entire lot shows that a CN algorithm quantifies progression evaluation with a very fine accuracy, surpassing functional parameters’ variations. The CN algorithm can also be successfully used for early detection, mainly on the ground glass opacity Hounsfield Units band of the scan.

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Section: Discussionmentioning

confidence: 99%

“…A mixture of pattern matching and analyzing techniques, the complex network approach [26] might provide insights previously unexplored by the other CADs.…”

Section: Computer Aided Diagnosismentioning

confidence: 99%

Section: Hypothesis To Be Exploredmentioning

confidence: 99%

Section: Computer-enhancing the Datamentioning

confidence: 99%

See 2 more Smart Citations

Enhancing Imagistic Interstitial Lung Disease Diagnosis by Using Complex Networks

Trușculescu¹,

Manolescu²,

Broască³

et al. 2022

Preprint

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Interstitial lung diseases computer-aided imaging diagnosis, using complex networks

Adriana,

Versavia,

Laura

et al. 2023

Pneumologia

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The article aims to explore how a Complex Network (CN) computer-aided technique targeted for interstitial lung disease (ILD) approach can enhance the work of clinicians and if a CN-based computer-aided diagnosis can provide new data to help manage ILDs more successfully. The CN technique is used to evaluate the progression of the disease by analyzing relevant axial HRCT slices and dynamic CN evaluation using the relative speed for each layer. The article presents the results from a study of 65 patients with interstitial lung disease (ILD), comprising 18 females with a mean age of 59.35 years (ranging from 34 to 76). The initial clinical diagnosis was idiopathic pulmonary fibrosis (IPF) in 28 patients (43.07%), Non-Specific Interstitial Pneumonia (NSIP) in 11 patients, and other ILDs in the remaining patients. Each CT scan fulfilled the criteria for high-resolution CT with constant characteristics across the group. All patients underwent imagistic follow-up for at least 11 months, and additional data were provided for each investigation. The cohort was chosen based on concordant lung function decline and imaging evolution decline. The article concludes that the complex network approach provides both a qualitative visual map and quantitative metrics to enhance ILD diagnosis and progression tracking. The results suggest that a CN-based computer-aided diagnosis can provide new required data to manage ILDs more effectively. This approach may enable clinicians to make more precise conclusions regarding the structure of the analyzed lung area, which can help tailor disease management strategies to individual patient profiles.

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A Novel Method for Lung Image Processing Using Complex Networks

Cited by 2 publications

References 25 publications

Enhancing Imagistic Interstitial Lung Disease Diagnosis by Using Complex Networks

Enhancing Imagistic Interstitial Lung Disease Diagnosis by Using Complex Networks

Interstitial lung diseases computer-aided imaging diagnosis, using complex networks

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