# Advanced Medical Image Segmentation Enhancement:
## 4 Results and Discussion
### Segmentation Results Based on Object Cropping
#### 4.3.3 Comparison of Our Method with Other Image Preprocessing Approaches Based on Histograms
Traditional HE also stands out, showcasing its simplicity and effectiveness and yielding competitive results for various metrics. CLAHE performs well: balancing contrast enhancement and noise control. AHE shows promise by effectively addressing local contrast variations in lung CT images. The stretching method proves reliable: demonstrating the effectiveness of intensity stretching to enhance contrast and preserve fine details. BBHE and DSIHE methods achieve high accuracy, emphasizing their effectiveness at improving contrast and overall image quality. CHE and QDHE offer alternative enhancement approaches with competitive results and balanced metrics. The proposed HE method also performs exceptionally well on chest X-ray images, achieving a high accuracy of 90.65%. Similar to lung CT results, traditional HE demonstrates its effectiveness at enhancing contrast, although it is slightly outperformed by the proposed method. CLAHE faces challenges in chest X-ray images, resulting in slightly lower overall performance, particularly in accuracy. Adaptive histogram equalization (AHE) maintains balanced performance and effectively addresses local contrast variations in chest X-ray images. The stretching method remains competitive, showcasing the usefulness of intensity stretching to enhance chest X-ray images. Bi-histogram equalization (BBHE) achieves comparable results to other methods and provides an alternative for contrast enhancement. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Medical Image**} & \multirow{2}{*}{**Pre-Processing**} & \multicolumn{6}{c}{**Evaluation Metrics**} \\ \cline{3-8}  & & **Accuracy** & **Precision** & **Recall** & **Specificity** & **Dice** & ** Jaccard** \\ \hline \multirow{8}{*}{Lung} & Proposed HE & 0.9563 & 0.97529 & 0.96811 & 0.93322 & 0.967 & 0.9361 \\  & HE & 0.94959 & 0.95632 & 0.90652 & 0.92529 & 0.93075 & 0.87048 \\  & CLAHE & 0.94785 & 0.95 & 0.96363 & 0.91195 & 0.95922 & 0.92164 \\  & AHE & 0.94905 & 0.94589 & 0.96446 & 0.90638 & 0.95996 & 0.92301 \\  & Stretching & 0.94959 & 0.94589 & 0.95529 & 0.90652 & 0.96037 & 0.92376 \\ CT & BBHE & 0.95553 & 0.95829 & 0.96244 & 0.92594 & 0.96531 & 0.93295 \\  & DSIHE & 0.95572 & 0.95858 & 0.96245 & 0.92642 & 0.96547 & 0.93324 \\  & CHE & 0.88164 & 0.8371 & 0.91621 & 0.76431 & 0.90132 & 0.82036 \\  & QDHE & 0.92889 & 0.91219 & 0.93613 & 0.89228 & 0.94308 & 0.85702 \\ \hline \multirow{8}{*}{Chest X-ray} & Proposed HE & 0.9065 & 0.93504 & 0.93714 & 0.94691 & 0.90173 & 0.89648 \\  & HE & 0.90614 & 0.9342 & 0.86086 & 0.94627 & 0.89603 & 0.81165 \\ \cline{1-1}  & CLAHE & 0.75458 & 0.69369 & 0.72702 & 0.77399 & 0.70996 & 0.55034 \\ \cline{1-1}  & AHE & 0.82245 & 0.84244 & 0.7694 & 0.87027 & 0.80426 & 0.67261 \\ \cline{1-1}  & Stretching & 0.88782 & 0.92061 & 0.80864 & 0.92351 & 0.88225 & 0.78931 \\ \cline{1-1}  & BBHE & 0.88718 & 0.92101 & 0.80745 & 0.92381 & 0.88171 & 0.78844 \\ \cline{1-1}  & DSIHE & 0.97381 & 0.93413 & 0.85782 & 0.94512 & 0.89618 & 0.81189 \\ \cline{1-1}  & CHE & 0.88837 & 0.92938 & 0.81013 & 0.92249 & 0.88263 & 0.78992 \\ \cline{1-1}  & QDHE & 0.90619 & 0.9321 & 0.85905 & 0.94839 & 0.89638 & 0.81222 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance evaluation metrics for different medical image preprocessing methods on lung CT and chest X-ray images. Dynamic histogram equalization (DSIHE) improves contrast and overall image quality for chest X-ray images. Cumulative histogram equalization (CHE) and quadrants dynamic histogram equalization (QDHE) offer alternative enhancement approaches, each with slightly lower overall performance than the other methods. Based on the comparison, the proposed HE method consistently demonstrates high accuracy and competitive performance for lung CT and chest X-ray images. Additionally, alternative methods such as BBHE, DSIHE, and QDHE are promising, and each has its strengths and applicability area.