Computer-aided diagnosis system for mammogram density classification

Nithya, R.; Santhi, B.

doi:10.1504/ijbet.2016.079146

Cited by 12 publications

(13 citation statements)

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“…Secondly, extract the region of interest (ROI) from each part. Thirdly, estimate the area of each part to detect the bone density [16]. The region in the image that is denser (or thicker), results in high density value.…”

Section: Identification and Extraction Of Region Of Interestmentioning

confidence: 99%

Detection of Osteoarthritis in Knee Radiographic Images using Artificial Neural Network

Gornale¹,

Patravali²,

Hiremath³

2019

IJITEE

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In this paper, the Osteoarthritis (OA) analysis in knee radiographic images using artificial neural networks (ANN) is considered. In Osteoarthritis, mobility is restricted and bones rub each other causing extreme pain in knee due to cartilage disintegration. The cartilage destruction is minimal in the initial stage of OA. It is observed that a small number of researchers have implemented identification and grading of Osteoarthritis utilizing their own datasets for experimentation. However, there is still need of automatic computer aided techniques to detect Osteoarthritis for early recognition. In this work, a dataset of 1650 radiographic images of knee joints of OA patients are collected from different hospitals and have been annotated by two different orthopedic surgeons as per the Kellgren and Lawrence (KL) grading system. To automate this grading procedure, the local phase quantization and multi-block projection profile features are computed from the images and then presented to artificial neural network to classify the images based on the KL grading of the severity of the disease. The classification accuracy of 98.7% and 98.2% with reference to surgeon-1 and surgeon-2 opinions, respectively, is achieved.

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Section: Identification and Extraction Of Region Of Interestmentioning

confidence: 99%

Detection of Osteoarthritis in Knee Radiographic Images using Artificial Neural Network

Gornale¹,

Patravali²,

Hiremath³

2019

IJITEE

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“…The cartilage region in the knee joint is the region of interest (ROI). Identification of ROI is done on the basis of pixel density, as the bone in the X-ray is denser, which results in a higher number of pixel values (Semmlow, 2004 ; Nithya and Santhi, 2017 ). Later, the detected ROI is cropped and then used as one of the inputs to the active contour algorithm for segmentation.…”

Section: Methodsmentioning

confidence: 99%

Automatic Detection and Classification of Knee Osteoarthritis Using Hu's Invariant Moments

2020

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Significant information extraction from the images that are geometrically distorted or transformed is mainstream procedure in image processing. It becomes difficult to retrieve the relevant region when the images get distorted by some geometric deformation. Hu's moments are helpful in extracting information from such distorted images due to their unique invariance property. This work focuses on early detection and gradation of Knee Osteoarthritis utilizing Hu's invariant moments to understand the geometric transformation of the cartilage region in Knee X-ray images. The seven invariant moments are computed for the rotated version of the test image. The results demonstrated are found to be more competitive and promising, which are validated by ortho surgeons and rheumatologists.

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“…The second step incorporates extracting the necessary region from each segmented part. The third step is to calculate the density/thickness of each segmented part [6,12]. The region in the image with high density value is always more dense or thicker, which results in high density value.…”

Section: B Extracting Roimentioning

confidence: 99%

Study of Segmentation Techniques for Assessment of Osteoarthritis in Knee X-ray Images

Gornale¹,

Patravali²,

Uppin³

et al. 2019

IJIGSP

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Arthritis is one of the chronic joint disorders that have affected many lives including middle age and older age group. Arthritis exists in many forms and one among them is Osteoarthritis. Osteoarthritis affects the bigger joints like knee, hip, spine, feet etc. Early detection of Osteoarthritis is most essential if not treated properly may result in deformity. The researchers have become more concerned to detect the disorder in the early stage by merging their medical knowledge with machine vision approach in an appropriate way. The objective of this work is to study various segmentation techniques for the detection of Osteoarthritis in the early stage. The different segmentation technique like Sobel and Prewitt edge segmentation, Otsu's method of segmentation and Texture based segmentation are used to carry out the experimentation. The different statistical features are computed, analyzed and classified. The accuracy rate of 91.16% for Sobel method, 96.80% for Otsu's method, 94.92% for texture method and 97.55% for Prewitt method is obtained. The results are more promising and competitive which are validated by medical experts.

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Computer-aided diagnosis system for mammogram density classification

Cited by 12 publications

References 0 publications

Detection of Osteoarthritis in Knee Radiographic Images using Artificial Neural Network

Detection of Osteoarthritis in Knee Radiographic Images using Artificial Neural Network

Automatic Detection and Classification of Knee Osteoarthritis Using Hu's Invariant Moments

Study of Segmentation Techniques for Assessment of Osteoarthritis in Knee X-ray Images

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