An image preprocessing method for kidney stone segmentation in CT scan images

Thein, Nilar; Nugroho, Hanung Adi; Adji, Teguh Bharata; Hamamoto, Kazuhiko

doi:10.1109/cenim.2018.8710933

Cited by 29 publications

(13 citation statements)

References 3 publications

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“…CT images of 30 patients were studied. As a result, a 95.24% sensitivity value was obtained with the proposed algorithm [10]. In another study [11], the effects of morphological operations on kidney stone classification and analysis were investigated.…”

Section: Related Workmentioning

confidence: 96%

“…In the literature, preprocessing studies have been carried out to reduce this difficulty. In a study, a preprocess algorithm was developed for kidney stone detection and segmentation from CT images [10]. Three thresholding algorithms based on density, size, and location were applied to extract unrelated organ and bone structures from the images.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Kidney X-ray Images Classification using Machine Learning and Deep Learning Methods

Aksakalli

Kaçdioğlu

Hanay

2021

Balkan Journal of Electrical and Computer Engineering

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Today, kidney stone detection is performed manually by humans on medical images. This process is timeconsuming and subjective as it depends on the physician. This study aims to classify healthy or patient individuals according to the status of kidney stones from medical images using various machine learning methods and Convolutional Neural Network (CNN). We evaluated various machine learning methods such as Decision Trees (DT), Random Forest (RF), Support Vector Machines (SVM), Multilayer Perceptron (MLP), K-Nearest Neighbor (kNN), Naive Bayes (BernoulliNB), and deep neural networks using CNN. According to the experiments, the Decision Tree Classifier (DT) has the best classification result. This method has the highest F1 score rate with a success rate of 85.3% using the S+U sampling method. The experimental results show that the Decision Tree Classifier (DT) is a feasible method for distinguishing the kidney x-ray images.

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Section: Related Workmentioning

confidence: 96%

Section: Related Workmentioning

confidence: 99%

Kidney X-ray Images Classification using Machine Learning and Deep Learning Methods

Aksakalli

Kaçdioğlu

Hanay

2021

Balkan Journal of Electrical and Computer Engineering

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“…This is then segmented using Level set segmentation which is then analyzed to detect stone size and location. NilarThein et al (2018) have proposed an Image preprocessing method for the segmentation of kidney stones in CT scan images [15]. Here three thresholding algorithms that are based on intensity, size and location are applied for removing unwanted regions in CT images.…”

Section: B Analysis Of Nephrolithiasis Detection On Ct Imagesmentioning

confidence: 99%

Analysis and Detection of Nephrolithiasis using Imaging Techniques

Tripathy

Sivakumar

Nair

et al. 2021

International Journal of Biology and Biomedical Engineering

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Nephrolithiasis (kidney stone) is a disease which affects 7% of females and 11% of males at some stage in their life. Early identification of Nephrolithiasis is necessary to avoid complications. Imaging techniques form the basis for the detection of kidney stones and aid in locating the position, size, and the number of stones present in the renal structure. This paper reports an extensive analysis of recent trends in the detection of Nephrolithiasis using Imaging techniques. Since Computed Tomography (CT) and ultrasound imaging are commonly used in the medical field, analysis of both the methods is considered in this paper. The detailed study on various methodologies and algorithms that have been adopted on CT and ultrasound images in recent years in locating kidney stones, finding the exact size of the stones based on pixel count, enhancing image quality, obtaining better de-speckling, faster segmentation, and pre-processing of the renal images has been carried out. Based on the analysis, an artificial intelligence-based approach is proposed that will aid the medical practitioner for faster, accurate detection of Nephrolithiasis and a technique to reduce the exposure of radiation in Computed Tomography Imaging. Further, it is concluded that ultrasound techniques can be employed subsequently for preliminary diagnosis through CT if the medical practitioner recommends.

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“…Large object with high intensity value (stone and bone) are easier to detect and segment compared with small ones with low intensity values. Therefore, stone and bone can extracted among other soft tissue and organs using Otsu's thresholding and morphological operation is also used to enhance the output image [29]. This segmentation can only remove low intensity region and the result is contained all high intensity region (stone, calcification, bones and stent) as shown in Fig.…”

Section: Implementation Detailmentioning

confidence: 99%

Automated False Positive Reduction and Feature Extraction of Kidney Stone Object in 3D CT Images

Thein¹,

Adji²,

Hamamoto³

et al. 2019

IJIES

Self Cite

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CT imaging is widely used for a variety of diagnostic and therapeutic purposes. It is a best effective and efficient tool in kidney stones diagnostic and treatment strategies whenever contactless measurements of quality are necessary to examine different types of stones. Accurate segmentation is a key process in CT diagnostic imaging. But, there are some challenges to distinguish among stones, stents or nephrostomy tubes, calcification and bone fragments in stone diagnostic CT imaging. This study proposed and experimentally investigated a scheme to reduce false positive for stone detection and to extract meaningful structural information for diagnosis of kidney stones. Firstly, the interested object (stone) is segmented by Otsu's thresholding method and morphological operation. Secondly, the parameters based on 3D morphological features are measured for stone detection. In false positive reduction, the unwanted disturbances (bone fragments, image noise, calcification and stents) are eliminated using novel volume-ratio based thresholding and SAV ratio based thresholding. Finally, it provided an output that identifies meaningful structural information (Surface-area-to-volume ratio, volume, location and density of kidney stone) for kidney stone diagnosis. Digitized transverse abdomen CT images from 35 patients with kidney stone cases were statistically analyzed and validated. The estimation of coordinate points in the stone region was measured independently by the expert radiologists to get the validated data for the analysis. The proposed algorithm was significantly reduced the false positives with 70% of overall accuracy. Moreover, it can present the meaningful information of the kidney stone. This may lead to more accurate stone management.

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An image preprocessing method for kidney stone segmentation in CT scan images

Cited by 29 publications

References 3 publications

Kidney X-ray Images Classification using Machine Learning and Deep Learning Methods

Kidney X-ray Images Classification using Machine Learning and Deep Learning Methods

Analysis and Detection of Nephrolithiasis using Imaging Techniques

Automated False Positive Reduction and Feature Extraction of Kidney Stone Object in 3D CT Images

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