# An Efficient and Automatic Framework for Segmentation and Analysis of Tumor Structure in Brain MRI Images

K. Bhima, M. Neelakantappa, K. Dasaradh Ramaiah, A. Jagan

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K. Bhima, M. Neelakantappa, K. Dasaradh Ramaiah, A. Jagan. An Efficient and Automatic Framework for Segmentation and Analysis of Tumor Structure in Brain MRI Images. 7th International Conference on Computer, Communication, and Signal Processing (ICCCSP), Jan 2023, Chennai, India. pp.66-78, 10.1007/978-3-031-39811-7_6. hal-05363247

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# An Efficient and Automatic Framework for Segmentation and Analysis of Tumor Structure in Brain MRI Images

K. Bhima¹(✉), M. Neelakantappa², K. Dasaradh Ramaiah¹, and A. Jagan¹

¹ B V Raju Institute of Technology, Narsapur, Telangana, India
{bhima.k, dasaradh.k, jagan.amgoth}@bvrit.ac.in

² Vasavi College of Engineering, Hyderabad, India
m.neelakanta@gmail.com

**Abstract.** The medical image segmentation techniques are frequently used to diagnosis, tumor detection and determining anatomical structures in brain MRI image and further classifying the pathological regions for treatment planning in clinical analysis. Manual analysis of multi-spectral MRI images is erratic process due to wide-ranging of features and tissue types. Midst several segmentation techniques proposed in literature work, an automatic unified segmentation framework substantiate to be effective method for multi-spectral MRI images segmentation. The efficient and robust framework holds varied popular techniques to accomplish segmentation of multi-spectral MRI images. An Efficient and robust MRI segmentation framework is intended to conglomerate benefits of most popular SVM, Watershed and EM-GM techniques for automatic and accurate segmentation and diagnosis of tumor in multi-spectral MRI images. The SVM method is converts input space to high-dimensional space where multi-spectral MRI images image are typically linear and indivisible to compute best linear discriminant surface. The proposed Efficient and robust Framework has a comprehensive tumor analysis structure which comprise of three stages for identification, segmentation and extraction of disorder region in multi-spectral MRI images. The efficient and robust framework has automatic identification of disorder in MRI Images successfully tumor region segmentation. The stage-1 is focused on identification of input MRI Images and classify MRI Images into normal or disorder MRI Images with SVM method. The stage-2 purpose is to segment the tumor with Watershed based technique in disorder in MRI Images that is detected in Stage-1. In stage-3 has an EM-GM for tumor region extraction and approximation of tumor region in actual image. To demonstrate an effectiveness of the efficient and robust framework, the proposed framework is evaluated on simulated multi-spectral MRI images from standard open BraTS MRI dataset delivered by ground truth segmentation.

**Keywords:** SVM Method · Watershed Method · EM-GM Method · An efficient and robust framework · Bilateral Filter · Multi-spectral MRI images
# 1 Introduction

An automatic analysis and segmentation of MRI Image [1-3] is crucial step in medical image procession era for diagnosis and in advance treatment planning for tumor analysis. An automatic MRI image analysis and segmentations techniques are used in numerous health care systems and clinical applications. MRI has preeminent soft tissue contrast compared with other medical imaging modalities for imaging the brain [10, 11]. The three-stage automatic and rapid method for tumor analysis and segmentation using multimodal MRI images. Proposed efficient and robust framework entails of three stages: tumorous slice detection, tumor region extraction and tumor substructures segmentation i.e. SVM (Stage-1), WM (Stage-2) and EM-GM(Stage-3). The proposed three-stage tumor analysis process for tumorous area detection from MRI human head volumes [1-3]. The efficient and robust framework present classification of MRI automatically after that segmentation of tumor with Watershed Technique. Stage-1 the SVM classifier is used to classification with training and testing process [6-9] with 8 × 8 patches. An optimal features are selected with IFS method. Classify of MRI dataset into a normal or tumorous MRI and identifying tumor in Stage-1. Watershed method [4-6] is mostly used for segmentation of tumor from overlapping regions in MRI in Stage-2. Lastly, Stage-3 has a post metric assessment such as analysis and diagnosis of tumor region using EM-GM Method [12-14]. Current research on MRI have understood the growing and necessity of automatic segmentation but present techniques are supervising to develop a framework for automatically extract the features in MRI. Furthermore, present methods are barely offer uncertainty details allied through segmentation process. Preprocessing of input MRI by most popular bilateral filter [15-17] to smooth and enhance MRI quality and edges. In this work, an Efficient and robust multi-spectral MRI images segmentation framework is devoted for classifying diverse brain regions for more investigation.

Furthermore, proposed Efficient and robust framework has three stages SVM(Stage-1), WM(Stage-2) and EM-GM(Stage-3) to accomplish better results that also presents in details data of tumor regions in segmentation results.

# 2 An Efficient and Robust Framework

An Efficient and robust framework is developed to overcome the limitations of tumor segmentation with fusing of most popular SVM, Watershed and EM-GM techniques for automatic tumor segmentation and diagnosis of tumor in MRI. The Efficient and robust framework consist of automatic MRI image classification into tumors and non-tumors with SVM Method.

This work proposed a fully three-stage automatic SVM (Stage-1), WM (Stage-2) and EM-GM (Stage-3) as shown in Fig. 1, which is rapid technique for tumor detection and MRI segmentation for accurate detection and segmentation of tumor. Preprocessing [15, 16] is the preliminary stage used to remove noises which are present in the multi-spectral MRI images using bilateral filter.

Figure 1 demonstrates the proposed an efficient and robust framework in which a series of stages is accomplished that is pre-processing of multi-spectral MRI images, tumor segmentation and feature extraction in multi-spectral MRI images and analysis of
<figure><img src="image_4.png" /><figcaption>**Fig. 1.** Proposed Segmentation Framework for analysis and diagnosis of multi-spectral MRI images</figcaption></figure>

tumor region. The framework of proposed Efficient and robust is shown in Fig. 1. Comprehend of 3-stages which includes SVM(Stage-1), WM (Stage-2) and EM-GM (Stage-3) identification of tumor, tumor segmentation, and tumor extraction correspondingly. Initially, the stage-1 focuses SVM classification [6–9] and identification of disorder MRI. The stage-2 deals with segments tumor in MRI with Watershed method. Finally, third stage encompasses tumor area extraction and assessment of tumor. Figure 1 is describing the framework for extensive analysis and diagnosis of brain tumor which is in input MRI image.
## 2.1 SVM Classifier for Classification of MRI Dataset

An Efficient and robust framework is a comprehensive segmentation technique developed for multi-spectral MRI images in which prominent SVM method is used for efficient classification of the tumors MRI images. The foremost stages in this framework comprise of MRI preprocessing, feature extraction and classification of tumors multi-spectral MRI images. SVM is a supervised machine learning technique which is used to address binary classification problems [9]. SVM is most popular model with distinctive characteristics for extracting tumor features in multi-spectral MRI images with better efficiency and quickness for accurate segmentation. In multi-spectral MRI images segmentation for the brain is to accurately distinct the disorder parts of the brain and eliminate the connection amongst the disorder region and normal regions are essential task of SVM classification method. The binary SVM [6–9] which constructs the hyperplane that can used for a linear and nonlinear SVM. The functional Efficient and robust is tries for classification of multi-spectral MRI images with supervised SVM.

SVM training process that is dataset designed in the form of { (x1,y1),(x2,y2),..., (xn,yn)},yi∈{-1,1},xi∈Rd,

where a data point xi, yi is corresponding designed label and n represents the training samples.

The linear SVM finds the optimal solution using:

$$ 
\begin{aligned}
\text{Minimize} &= \left\{ \frac{1}{2} |w|^2 + C \sum_{i=1}^{n} \varepsilon_i \right\}, \quad \varepsilon_i \ge 0 \\
\text{Subject to} \quad & y_i(w^T x_i + b) \ge 1 - \varepsilon_i, \quad i = 1, 2, \dots, n
\end{aligned} 
\quad (1) $$

Here penalty value is C and normal vector is the w, scalar quantity is b.

Slack variables is $\in_i$.

If $\alpha_i > 0$ analogous xi are termed as support vector.

Here $\alpha_i$ denotes Lagrangian multiplier which is used to reduce the minimum problem.

The linear discriminate function can be expressed with the optimal hyperplane parameters w and b and is given as:

$$ f(x) = \text{sgn}\left(\sum_{i=1}^{n} \alpha_i y_i x_i^T x + b\right) \quad (2) $$

## 2.2 Watershed Method Technique for Tumor Region Extraction

An extension of the Watershed Method algorithm [4–6] for better boundaries adherence and extraction of tumor. An Efficient and robust multi-spectral MRI images segmentation framework consist of most popular watershed method to overcome the limitation of MRI segmentation problems such as oversegmentation of MRI images and insufficient for finding of brain tumor in low contrast boundaries. A Watershed Method [3–5] is
robust for similarity measure based on content and border information for tumor region aggregations. A tumor extraction which is region aggregations and an adaptive merge threshold criterion (Fig. 2).

<figure><img src="image_5.png" /><figcaption>Fig. 2. Watershed Method for tumor region extraction in multi-spectral MRI images</figcaption></figure>

## 2.3 Analysis and Diagnosis of Tumor Structure with EM-GM Method

Proposed Framework for Efficient and robust has a multi-structure Expectation-Maximization technique is used for the subdivision and analysis of the tumor. Proposed Framework for efficient and robust method has presented a number of developments to conventional EM techniques which are most established for MRI segmentation [12–14]. EM techniques [12–14] is an iterative which has to compute the maximum likelihood values to presented statistical model such as Expectation and Maximization (Fig. 3).

Figure 4 is signifying the analysis sub tumor region and identification of numerous tumor type with EM-GM Method in multi-spectral MRI images.

The Fig. 5 is presents the analysis of sub tumor region with EM-GM method on multispectral brain MRI images. EM techniques is most popular for dissemination of intensities with fusion of weighted Gaussians features for classification of tumors.
<figure><img src="image_6.png" /><figcaption>**Fig. 3.** EM-GM framework for analysis and diagnosis of tumor structure in multi-spectral MRI images</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>**Fig. 4.** Analysis sub tumor region and identification of tumor type with EM-GM Method</figcaption></figure>

# 3 Results and Discussion

Performance of the Efficient and robust framework i.e. SVM method (Stage-1), Water-shed Method (Stage-2) and EM-GM method (Stage-3) are assessed and input images are obtained from open dataset for experimental and quantification of results, it has four multi-spectral bands i.e. T1 MRI, T1C MRI, T2 MRI and FLAIR MRI images. The proposed Efficient and robust is experimentally evaluated on challenge MRI dataset
<figure><img src="image_8.png" /><figcaption>Fig. 5. Analysis of sub tumor region with EM-GM Method</figcaption></figure>

with segmentation accuracy. The challenge datasets which are congregated from stan-
dard open MRI dataset origins are used for assessment and quantification of proposed
Efficient and robust.

The Table 1 and Fig. 6 is representation of assessable parameters are compared between proposed technique and present techniques for tumorous multi-spectral MRI images identification in open datasets with SVM method.

The Table 2 is demonstration of most popular Watershed method for tumor extrac-
tion in 10 patient’s HGG (high-grade gliomas) MRI and 10 patient’s LGG (low-grade
gliomas) MRI images. The obtained segmentation results are achieved enhancement for
tumor region extraction on multi sequence 20 patients MRI image.

The Table 2 and Fig. 7 is presentation of obtained segmentation results on HGG and
LGG MRI from multi-spectral MRI images dataset with watershed method.

The Table 3 and Fig. 8 is illustration of assessable parameters are compared between proposed technique and present techniques for EM-GM Method for analysis of tumor region. Experimental results present better outcomes as compared with available methods.

The proposed an efficient and robust framework is relatively expected to produce better segmentation results compared with the contemporary mentioned techniques. The proposed an efficient and robust framework extracts better tumor detail to distinguish
<table><caption>**Table 1.** Comprehensive SVM Classification Accuracy achieved on input datasets for identification of tumors MRI</caption><thead><tr><th rowspan="2">MRI Dataset</th><th rowspan="2">Data size</th><th colspan="2">SVM Classification Accuracy (%)</th></tr><tr><th>Existing</th><th>EFFICIENT AND ROBUST Framework</th></tr></thead><tbody><tr><td>MS_DS001</td><td>974</td><td>98.35</td><td>99.51</td></tr><tr><td>MS_DS002</td><td>885</td><td>98.21</td><td>99.18</td></tr><tr><td>MS_DS003</td><td>955</td><td>97.69</td><td>98.68</td></tr><tr><td>MS_DS004</td><td>930</td><td>98.17</td><td>99.08</td></tr><tr><td>MS_DS005</td><td>966</td><td>98.72</td><td>99.18</td></tr><tr><td>MS_DS006</td><td>873</td><td>97.82</td><td>98.20</td></tr><tr><td>MS_DS007</td><td>927</td><td>98.07</td><td>99.68</td></tr><tr><td>MS_DS008</td><td>971</td><td>98.27</td><td>99.08</td></tr><tr><td>MS_DS009</td><td>978</td><td>97.51</td><td>98.58</td></tr><tr><td>MS_DS010</td><td>903</td><td>98.65</td><td>99.08</td></tr></tbody></table>

<figure><img src="image_9.png" /><figcaption>**Fig. 6.** Performance analysis of SVM for tumors and non-tumors classification</figcaption></figure>

the tumor properly. The multi-spectral MRI images features are successfully extracted by the proposed framework for tumor analysis and diagnosis.

The projected an efficient and robust framework is tested on BRATS datasets and may be extended to further open databases.
<table><caption>**Table 2.** Quantitative segmentation results of tumor extraction from multi-spectral MRI images</caption><thead><tr><th rowspan="2">SNO</th><th rowspan="2">MRI Image</th><th rowspan="2">MRI Sequence</th><th colspan="3">Extraction of tumor region from MRI</th></tr><tr><th>Sensitivity (%)</th><th>Specificity (%)</th><th>Accuracy (%)</th></tr></thead><tbody><tr><td>1</td><td>HG0001</td><td>T1</td><td>99.17</td><td>98.31</td><td>98.05</td></tr><tr><td>2</td><td>HG0002</td><td>T1</td><td>98.14</td><td>98.9</td><td>98.17</td></tr><tr><td>3</td><td>HG0003</td><td>T1</td><td>99.01</td><td>98.25</td><td>98.97</td></tr><tr><td>4</td><td>HG0004</td><td>T1</td><td>98.11</td><td>98.01</td><td>98.72</td></tr><tr><td>5</td><td>HG0005</td><td>T1</td><td>97.23</td><td>98.89</td><td>97.89</td></tr><tr><td>6</td><td>HG0006</td><td>T1C</td><td>96.37</td><td>97.29</td><td>98.31</td></tr><tr><td>7</td><td>HG0007</td><td>T1C</td><td>97.62</td><td>97.83</td><td>98.64</td></tr><tr><td>8</td><td>HG0008</td><td>T1C</td><td>98.28</td><td>97.21</td><td>99.02</td></tr><tr><td>9</td><td>HG0009</td><td>T1C</td><td>97.54</td><td>98.01</td><td>98.17</td></tr><tr><td>10</td><td>HG0010</td><td>T1C</td><td>97.27</td><td>96.81</td><td>98.32</td></tr><tr><td>11</td><td>LG0001</td><td>T2</td><td>98.81</td><td>98.88</td><td>99.08</td></tr><tr><td>12</td><td>LG0002</td><td>T2</td><td>96.29</td><td>97.41</td><td>98.19</td></tr><tr><td>13</td><td>LG0003</td><td>T2</td><td>98.27</td><td>97.01</td><td>98.96</td></tr><tr><td>14</td><td>LG0004</td><td>T2</td><td>99.01</td><td>99.82</td><td>98.72</td></tr><tr><td>15</td><td>LG0005</td><td>T2</td><td>97.26</td><td>98.19</td><td>98.86</td></tr><tr><td>16</td><td>LG0006</td><td>FLAIR</td><td>98.11</td><td>97.83</td><td>98.19</td></tr><tr><td>17</td><td>LG0007</td><td>FLAIR</td><td>97.09</td><td>97.18</td><td>98.21</td></tr><tr><td>18</td><td>LG0008</td><td>FLAIR</td><td>98.19</td><td>97.18</td><td>98.57</td></tr><tr><td>19</td><td>LG0009</td><td>FLAIR</td><td>98.32</td><td>97.81</td><td>98.29</td></tr><tr><td>20</td><td>LG0010</td><td>FLAIR</td><td>98.21</td><td>98.15</td><td>97.49</td></tr></tbody></table>
<figure><img src="image_10.png" /><figcaption>Fig. 7. Performance analysis of Watershed method on multi-spectral MRI images</figcaption></figure>

<table><caption>**Table 3.** Analysis and diagnosis of tumor structure quantitative results for tumor segmentation</caption><thead><tr><th rowspan="2">MRI Image</th><th colspan="3">Segmentation accuracy(%)</th></tr><tr><th>Active Tumor</th><th>Necrosis</th><th>Edema</th></tr></thead><tbody><tr><td>HG0001</td><td>96.35</td><td>97.25</td><td>98.13</td></tr><tr><td>HG0002</td><td>97.62</td><td>98.81</td><td>95.62</td></tr><tr><td>HG0003</td><td>98.27</td><td>98.20</td><td>98.71</td></tr><tr><td>HG0004</td><td>98.79</td><td>98.87</td><td>98.05</td></tr><tr><td>HG0005</td><td>97.21</td><td>97.81</td><td>97.87</td></tr><tr><td>LG0001</td><td>96.18</td><td>95.83</td><td>93.43</td></tr><tr><td>LG0002</td><td>97.01</td><td>95.92</td><td>91.56</td></tr><tr><td>LG0003</td><td>96.13</td><td>96.15</td><td>93.92</td></tr><tr><td>LG0004</td><td>92.87</td><td>92.89</td><td>97.32</td></tr><tr><td>LG0005</td><td>95.91</td><td>96.17</td><td>97.41</td></tr></tbody></table>
<figure><img src="image_11.png" /><figcaption>Fig. 8. Performance analysis of analysis of tumor region with EM-GM Method</figcaption></figure>

## 4 Conclusion

The MRI segmentation is essential step for analysis but complicated procedure for automatic tumor segmentation in multi-spectral MRI images for clinical and health care applications. The Efficient and robust Framework for tumor detection, Segmentation and diagnosis from multi-spectral MRI images is developed, implemented, and tested on best conventional multi-spectral MRI images datasets. Tumorous MRI images are identified from MRI datasets with SVM method in first stage and the tumor parts were localized and classified. The second stage comprises extraction of tumor region automatically using Watershed method from classified tumorous multi-spectral MRI images. Stage three covers the post metric validation for analysis and diagnosis of tumor region. Based on segmentation results of quantitative and qualitative validation the proposed efficient and robust framework can be used for accurately detect tumorous MRI images, analyze and segment a tumor in multi-spectral MRI images. More techniques are discussed and offered to extend the work for inclusion of the likelihood of conglomerate numerous segmentation techniques in order to present enhanced and precise results for tumor detection in multi-spectral MRI images.
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