Show Summary Details
More options …

# Open Physics

### formerly Central European Journal of Physics

Editor-in-Chief: Seidel, Sally

Managing Editor: Lesna-Szreter, Paulina

IMPACT FACTOR 2018: 1.005

CiteScore 2018: 1.01

SCImago Journal Rank (SJR) 2018: 0.237
Source Normalized Impact per Paper (SNIP) 2018: 0.541

ICV 2017: 162.45

Open Access
Online
ISSN
2391-5471
See all formats and pricing
More options …
Volume 15, Issue 1

# Relationship between solitary pulmonary nodule lung cancer and CT image features based on gradual clustering

Weipeng Zhang
Published Online: 2017-06-16 | DOI: https://doi.org/10.1515/phys-2017-0044

## Abstract

### Background

The relationship between the medical characteristics of lung cancers and computer tomography (CT) images are explored so as to improve the early diagnosis rate of lung cancers.

### Methods

This research collected CT images of patients with solitary pulmonary nodule lung cancer, and used gradual clustering methodology to classify them. Preliminary classifications were made, followed by continuous modification and iteration to determine the optimal condensation point, until iteration stability was achieved. Reasonable classification results were obtained.

### Results

the clustering results fell into 3 categories. The first type of patients was mostly female, with ages between 50 and 65 years. CT images of solitary pulmonary nodule lung cancer for this group contain complete lobulation and burr, with pleural indentation; The second type of patients was mostly male with ages between 50 and 80 years. CT images of solitary pulmonary nodule lung cancer for this group contain complete lobulation and burr, but with no pleural indentation; The third type of patients was also mostly male with ages between 50 and 80 years. CT images for this group showed no abnormalities.

### Conclusions

the application of gradual clustering methodology can scientifically classify CT image features of patients with lung cancer in the initial lesion stage. These findings provide the basis for early detection and treatment of malignant lesions in patients with lung cancer.

PACS: 87.85.-d

## 1 Introduction

Lung cancer is the malignant tumor with highest morbidity and mortality in the world. The formal expression of most lung cancers are solitary pulmonary nodule in the early stage [1, 2]. These nodules are usually quasi-circular lesions with diameters less than 3 cm, which have different shapes, indefinite distribution locations and easy adhesion with other organizations [3]. Clinical symptoms do not usually exist, and the features are not very obvious on the CT image, therefore, determining whether the nodule is benign or malignant is a key to prevention of secondary lung cancer [4, 5]. In automatic identification of a solitary pulmonary nodule, understanding the feature extraction and expression of pulmonary nodule in CT images are the key point.

This paper takes advantages of methods based on fuzzy clustering of medical signs of early-stage lung cancer; using this combination, we hope to achieve early detection and timely treatment of malignant lesions. Frequently used fuzzy clustering methods include the dynamic clustering method, systemic clustering method and fuzzy C-mean value algorithm. This paper adopts dynamic clustering, which is also called the gradual clustering algorithm of fuzzy clustering. Gradual clustering has the advantages of small computational effort, small computer memory space requirements, and flexibility.

At present, as an important technology in data mining, gradual clustering is widely used in medicine. Gradual clustering analysis is often used to provide methodology for classification for traditional Chinese medicine (TCM) clinic treatment based on syndrome differentiation [6]. For example, Zhang Mingxue, et al. applied statistical methods such as clustering analysis to results and summed up the types and characteristics of syndromes in four phases of coronary heart disease complicated with hypertension [7]. Dynamic clustering analysis is also used for studies related to medication rules and screening of medication regimens in prescriptions, etc. Zhou Lu et al. applied a clustering analysis method in fuzzy mathematics to discuss the mutual relations between the TCMs relieving an exterior syndrome [8]. Fuzzy dynamic clustering analysis is also applied in medical image processing; for example, Tian Jie et al. applied this method to three-dimensional medical-image processing and analysis, including CT, spiral CT and MRI, thus better identifying thin bones and bones at the articulated joints; after rebuilding, the 3D model can clearly reproduce the anatomical structure [9].

## 2.1 Inspection method

CT scans were executed on patients separately. The CT scanner with 64 rows of spiral produced by USA GE Health care has been adopted to allow inspection and diagnosis on patients. Through a mediastinum window, the scans allow measurement of the maximum diameter of the lesion and evaluation of the lobulated calcification condition. The window width was set as 300∼450 HU, and the window center was set up to be 30∼50 HU. Through a lung window, the morphological characters of burr tumor, lung interface and cavity of the patients’ lesion were evaluated. The window width was set to 1500∼2000 HU, and the window center was set up as 450∼550 HU. The diagnosed results were compared with pathological examination results of the patients or other results from laboratory inspection.

## 2.2 Research data and feature extraction

The pulmonary nodule images came from a comprehensive hospital. Fifty-six cases of lung cancer and 2240 pieces of CT images of pulmonary nodules with diameters 3 mm≤d≤30 mm were collected. The images were in DICOM format. The images were viewed through DICOM medical image browser software, and from the region of interest of lung CT images which includes the shape of pulmonary nodules, texture features and other features, etc. [10, 11]. The image analysis was executed on the key medical signs so as to achieve the feature extraction from the region of interest; the extracted features were: burr, lobulation, cavity, calcification, uniform density and sunken pleura. Details of the CT image features of solitary pulmonary nodule lung cancers studied are in Table 1 (among which: let male=1, female=0; lobulation=3, no lobulation=4; burr=5, no burr=6; cavity=7, no cavity=8; calcification=9, no calcification=10; uniform density=11, non-uniform density=12; pleural indentation=13; non-pleural indentation=14).

Table 1

CT image features of solitary pulmonary nodule lung cancer

## 2.3 Application process of stepwise clustering

Gradual clustering works to make sum of squares of deviations within the sample groups reach a minimum standard. Through repeated adjustment of the number of individuals in each sample group, the optimization object, which is the maximum homogeneity (or the minimum heterogeneity) and maximum heterogeneity (or the minimum homogeneity) in sample groups, can be achieved. In the process of gradual clustering, this method has a rough classification of the samples at first, which is called initial classiffication, then repeated and continuous modification is executed in accordance with an optimization principle until reasonable classification is achieved [12].

In accordance with different analysis objects, clustering is divided into Q type and R type. Q type clustering is used to make classification processing on the sample, and R type clustering is to make classification processing on the variable [13]. This paper uses gradual clustering analysis on Q type samples.

## 2.3.1 Data conversion

As the dimensions of each factor in the system may not be the same, comparison is difficult to achieve, thus when the association analysis is in progress, treatment usually is carried out to make it nondimensionalized, so as to remove the influence brought by the each index dimension. A “Standardization” method is adopted in this paper to process the data: $xik=yik−y¯kSk(i=1,2,⋯,n,k=1,2,⋯,p)$(1)

In which:${\overline{y}}_{k}=\frac{1}{n}\sum _{i=1}^{n}{y}_{ik},{{S}_{k}}^{2}=\frac{1}{n-1}\sum _{i=1}^{n}\left({y}_{ik}-{\overline{y}}_{k}{\right)}^{2}$, n is the sample number, p is the observed variable number, and in this paper, n=56, p=8. The standardized data are in Table 2.

Table 2

Standardized data

## 2.3.2 Initial clustering by rounding through conversion method

The method of integer transformation is adopted in initial classification. For each sample Xij, let $SUM(i)=∑j=1mXij$(2)

SUM(i) represents the index variable value sum of each sample (m is the index variable number). If all samples are going to be classified, the calculation shall be made to each sample: $[(K−1)(SUM(i)−MI)MA−MI]+1$(3)

If the integer adjacents to such a number is k, then the sample Xi shall be classified to k category (1 ≤ kK). We can obtain from Table 2, in this case,$MI=\underset{1\le i\le n}{min}SUM\left(i\right)=-9.054,MA=\underset{1\le i\le n}{max}SUM\left(i\right)=7.5542$, [.] means rounding operation. As for the selection of the k value, based on the medical acknowledge and the repeated computerized debugging tests, it is appropriate to divide 56 samples into three groups in the initial stage, and the initial clustering shall be executed in DPS software system in accordance with formula (2) and (3).

## 2.3.3 Selection of condensation point

The condensation point is the point representing the centre of the class to be formed. The selection of the condensation point can greatly influence the classification results. A centroid method [14] is adopted in this paper. Firstly, the objects are artificially divided into several categories, then the gravitational center of each category is calculated so as to be the gravitational center of clustering. The mean value of samples in such a category is taken as the condensation point, with formula: $gj=1nk∑Xij′$(4)

Among which, gj(j = 1, 2, ···, m) shall be the barycentric coordinate of category k(1 ≤ kK), nk is the sample number of category k. Therefore, in accordance with formula (4), the gravity center of initial classification is obtained, then the condensation point is obtained, and the initial classification’s barycentric coordinate is in Table 3.

Table 3

Initial cl|assification

## 2.3.4 Cluster all samples based on the latest condensation point

The objective function S is defined as: $S(i)=∑i=1mni(x¯i−x¯)′(x¯i−x¯)$(5)

Here, ni is the number of items in sample group i, i is the mean value, and is the sum mean value of sample N, m represents numbers of the grouping of N samples. $N=\sum _{i=1}^{m}{n}_{i},and$ S is the distance between sample and category condensation point.

The distance from each xi sample to each category condensation point is calculated, and the sample is classified into the category occupied by the nearest condensation point.

## 2.3.5 Modification clustering, making the clustering reasonable

After the initial classification takes shape, it needs to be modified, step by step. The different methods of dynamic clustering are distinguished mainly by different principles of modification and classification. There are two methods of modification and classification, the one-by-one method and the group-by-group method [15-17]. This paper adopts the group-by-group method. After the initial condensation points are selected, each sample is classified according to its nearest condensation point. Each condensation point constitutes a class by itself, with its nearest points belonging to that class. The class’s centre of gravity is then recalculated, with the new value replacing the previous condensation point. Additional samples are then classified and the procedure is repeated until all samples have been placed into a class. When the calculated center of gravity is the same as the original condensation point, the process is stopped. If the center of gravity does not match the original condensation point, the previous steps are repeated according to objective function S till agreement is reached

## 3 Analysis of results

Gradual clustering is judged based on a minimum sum of squares of deviations in the samples group. Higher homogeneity in a group is realized by repeatedly adjusting the iterations. This paper used data from 56 patients of solitary pulmonary nodule lung cancer as samples; their sex, age and CT image features are used as indices. The sample data were sorted into three classes by gradual clustering, as shown in Figure 1, the numbers on y-axis represent the individual samples.

Figure 1

Tree diagram of gradual clustering for CT image features of solitary pulmonary nodule lung cancer

Different features relating to the three classes of patient CT images are as follows:

1. Mostly female patients with ages between 50 and 65 years. CT images of solitary pulmonary nodule lung cancer for this group show that in the pulmonary nodules there are complete lobulation and burr, texture density is homogeneous, cavitation and calcification are not found, but pleural indentation signs exist. CT images with such characteristics accounts for 81% of the total images collected.

2. Mostly male patients with ages between 50 and 80 years. This group’s CT images show that in the pulmonary nodules there are complete lobulation and burr, texture density is homogeneous, cavitation, calcification and pleural indentation are not found.

3. Mostly male patients with ages between 50 and 80 years. This group’s CT images show that in the pulmonary nodules there are no lobulation and burr, texture density is homogeneous, cavitation, calcification and pleural indentation are not be found.

## 4 Discussion

It is not necessary for gradual clustering to calculate the similarity coefficient matrix between all samples. It is only necessary to calculate the distance between each sample and the center of clustering, which is the same as calculating the sum of squares of deviations. This process can therefore greatly shorten the calculation time and memory requirements of the computer so as to improve work efficiency. Our results show that with the gradual clustering analysis, CT images for patients with solitary pulmonary nodule lung cancer can be classified into three types according to the similiarity of features of CT images. The method of gradual clustering is available for discovering the features of patients CT images similarities, distinguishing images features differences, satisfying the completeness, but not losing the information. All are beneficial to increase the accuracy of inspection and diagnosis, decrease the false positive rate of pulmonary nodule identification, and provide early information about pathological changes for doctors to help them understand medical features of CT images of solitary pulmonary nodule lung cancer.

Some errors may have arisen in our analysis; possible sources of error are discussed below.

1. A limited range of samples have been used. The gradual clustering analysis in this paper was restricted to lung cancer patients hospitalized in the comprehensive hospital. Additional samples from other areas, and additional CT image features would increase the significance of the results.

2. The outlier value and improper clustering variables have little influence on the clustering results of graudual clustering. Improper initial clustering can be repeatedly adjusted. The process is, however limited because gradual clustering results are very sensitive to the initial clustering.

3. The performance of clustering algorithm is closely linked with data, and there is no single algorithm that works for all cases. At present, each clustering algorithm put forward by the researchers has its own advantages, disadvantages and specific range of application. For similar data sets, use of different clustering algorithms result in different results of division.

In conclusion, the author of this paper has classified medical features in CT images medical features of patients with solitary pulmonary nodule lung cancer. The method of gradual clustering is applied to analysis CT image features, the clustering of patients’ CT image medical features of solitary pulmonary nodule lung cancer can be obtained, which will be an important reference to doctor’s diagnosis. Further research is needed into obtaining strong association rules with high reliability in order to improve the accuracy of disease diagnosis.

## Acknowledgement

This project was supported by the National Spark Program funding project (No. 2015GA701023), Science and technology project of enriching the people of Ningbo city (No. 2015C10043, 2016A10041) and Zhejiang education program (No. 2015SCG087).

## References

• [1]

Xing Q.Q., Liu Z.X., Lin B.Q., Qian J., Cao L., Burr Inspection and Quantitative Evaluation of CT Image of Pulmonary Nodule, J.Comput. App., 2014, 34, 3599-3604. Google Scholar

• [2]

Li H., Jiang C.X., Ning P.G., Kan X.J., Chen C.Y., Wu M.G., et al., Diagnostic value of CT Image with High Revolution of Solitary Pulmonary Nodule, J. Zhengzhou Univ. Med. Sci., 2014, 49, 872-875. Google Scholar

• [3]

Pei X.M., Guo H.Y., Dai J.P., Pulmonary Nodule Identification of Fuse Pixel Space Information and Weighting Fuzzy Clustering, J. Northeastern Univ., 2013, 31, 1215-1253. Google Scholar

• [4]

Xu X.Q., Zhou Z.J., Su J.L., Liu G.X., Morbidity Rate of Solitary Pulmonary Nodule and Relative Factors Analysis, Shanxi Med. J., 2013, 42, 1222-1223. Google Scholar

• [5]

Ou Z.R., Tao L.Q., Shi G.C., Wan H.Y., Image features of Solitary Pulmonary Nodule and Comparison of Two Cancers Prediction Model, Chin. J. Respir. Cr. Care Med., 2012, 11, 168-171. Google Scholar

• [6]

Su X.Y., Application of Data Mining Clustering Analysis Method in TCM Clinic, Pract. Clin. J. Integr. Tradit. Chin. West Med., 2010, 10, 90-93. Google Scholar

• [7]

Zhang M.X., Li J., Li H., Yi D.H., Study on TCM Syndrome Characteristics of Coronary Heart Disease Complicated with Hypertension based on Clustering Analysis, Chin. Arch. Tradit. Chin. Med., 2016, 34, 1543-1546. Google Scholar

• [8]

Zhou L., Tang X.Y., Fu C., Peng S.H., Fuzzy Clustering Analysis on TCMs Relieving Exterior Syndrome, West China J. Pharm. Sci., 2004, 19, 339-341. Google Scholar

• [9]

Wen Z.W., Wu X.M., Guo S.W., Retrieval Method of Medical Images based on Fuzzy Clustering, Chin. J. Med. Physics, 2007, 3, 180-183. Google Scholar

• [10]

Wang J.J., Sun T., Zhao F.C., Li X., Cai B.W., Zhu X.M., Guo X.H., Application of Support Vector Machine in CT Image of Pulmonary Nodule, Beijing Biomed Eng., 2013, 32, 528-530, 535. Google Scholar

• [11]

Zhang Z.W., Zhang C.Q., Wang G.L., Zhang C.Q., Computer-aided Diagnosis of Solitary Pulmonary Nodule in HDCT, J. Med. Imaging, 2015, 25, 993-997. Google Scholar

• [12]

Chang C., Feng P., Sun D.M., Zhang K., Growth Prediction of Floating Algae in Reservoir Based on Gradual Clustering Analysis, Chin. Environ. Sci., 2015, 35, 2805-2812. Google Scholar

• [13]

Zeng X.M., Wang L.Y., Wu W.P., Guan Y.Y., Fang Q., Clustering Analysis of Cystic Hydatidosis in Non-Tibet Plateau Epidemic Area of China, Chin. J. Schistosomiasis Ctrl., 2014, 26, 180-183. Google Scholar

• [14]

Tang Q.Y., Feng M.G., Practical Statistic Analysis and DPS Data Processing System, 3rd ed., Science Press, Beijing, 2002. Google Scholar

• [15]

Du T.S., Huang J.L., Application of Dynamic Clustering in Crop Remote Sensing Yield Estimation Zoning of Hubei, J. Huazhong Normal Univ. Sci., 2000, 34, 241-244. Google Scholar

• [16]

Maria R., Maria L.G., Multiplier method and exact solutions for a density dependent reaction-diffusion equation, Appl. Maths. Nonlin. Sci., 2016, 1, 311-320.Google Scholar

• [17]

Vishwanath B.A., Shankar N., Mahesh K.N., Multigrid method for the solution of EHL line contact with bio-based oils as lubricants, App. Maths. Nonlin. Sci., 2016, 1, 359-368. Google Scholar

Accepted: 2016-09-06

Published Online: 2017-06-16

Citation Information: Open Physics, Volume 15, Issue 1, Pages 400–404, ISSN (Online) 2391-5471,

Export Citation