Using third-party intellectual properties (3PIP) has been a norm in IC design development process to meet the time-to-market demand and at the same time minimizing the cost. But this flow introduces a threat, such as hardware trojan, which may compromise the security and trustworthiness of underlying hardware, like disclosing confidential information, impeding normal execution and even permanent damage to the system. In years, different detections methods are explored, from just identifying if the circuit is infected with hardware trojan using conventional methods to applying machine learning where it identifies which nets are most likely are hardware trojans. But the performance is not satisfactory in terms of maximizing the detection rate and minimizing the false positive rate. In this paper, a new hardware trojan detection approach is proposed where gate-level netlist is segmented into regions first before analyzing which nets might be hardware trojans. The segmentation process depends on the nets' connectivity, more specifically by looking on each fanout points. Then, further analysis takes place by means of computing the structural similarity of each segmented region and differentiate hardware trojan nets from normal nets. Experimental results show 100% detection of hardware trojan nets inserted on each benchmark circuits and an overall average of 1.38% of false positive rates which resulted to a higher accuracy with an average of 99.31%.