Noise reduction effect of computed tomography by image summation method (fused CT): Phantom study

Kobayashi, Tomoya; Yoshida, Masahiro; Numano, Tomokazu; Shiotani, Seiji; Saitou, Hajime; Tashiro, Kazuya; Someya, Satoka; Kaga, Kazunori; Miyamoto, Katsumi; Hayakawa, Hideyuki

doi:10.1016/j.fri.2020.200418

Cited by 7 publications

(2 citation statements)

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“…below. According to the value of intensity, the data point quality is calculated, and the probability density function f d (P 0 ) is defined in Equation (6).…”

Section: Data Preprocessingmentioning

confidence: 99%

“…Further, the enhancement method is to improve the contrast between the background and region of interest 6 with spatial filtering to improve the edge detection process by removing blur 7 and color normalization to minimize color variation in the image 8 . Intensity centering and histogram equalization are mostly employed to normalize different histopathology pictures.…”

Section: Introductionmentioning

confidence: 99%

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Extreme learning machine algorithm‐based model for lung cancer classification from histopathological real‐time images

John

Baskar

2023

Computational Intelligence

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In the present scenario, developing an automatic and credible diagnostic system to analyze lung cancer type, stage, and level from computed tomography (C.T.) images is a very challenging task, even for experienced pathologists, due to the nonuniform illumination and artifacts. The nonuniform illumination and artifacts are the low‐frequency changes in image intensity that arise from the sensor and the person's movement while recording the C.T. scanned images. Although numerous machine learning techniques are used to improve the effectiveness of automatic lung cancer diagnostic systems, the classification accuracy of these systems still needs significant improvement to satisfy the real‐time requirement of the diagnostic situations. A new extreme learning machine (ELM) algorithm‐based model (hereafter called XlmNet) is proposed to classify the histopathology scans effectively. XlmNet utilizes The Cancer Imaging Archive (TCIA) dataset. After data collection, the initial stage in XlmNet is preprocessing, including noise removal, histogram equalization, and quality‐improved image. The enhanced Profuse Clustering (EPC) method is implemented for segmenting the affected regions from C.T. scans by image segment using superpixel clustering. The statistical attributes are extracted by using Principal Component Analysis (PCA). ELM classifier helps in classifying the lung nodules. The empirical results of the XlmNet model are related to some advanced classifiers concerning performance metrics. The evaluations of XlmNet on the TCIA dataset reveal that XlmNet outperforms other classification networks with the Accuracy of 0.965, a sensitivity of 0.964, a specificity of 0.865, a precision of 0.962, a Jaccard similarity score (JSS) of 0.95.

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“…below. According to the value of intensity, the data point quality is calculated, and the probability density function f d (P 0 ) is defined in Equation (6).…”

Section: Data Preprocessingmentioning

confidence: 99%